METHOD AND SYSTEM FOR MODELING MULTI-WELL COMMUNICATION CONSIDERING MULTIPLE FLOW REGIMES FOR HYDROCARBON MANAGEMENT

Abstract

A methodology for modeling multi-well communication considering multiple flow regimes for hydrocarbon management is disclosed. Understanding well communication scenarios in unconventional reservoirs may assist in assessing hydrocarbon asset development planning. Typically, well communication is determined by using a suitably-designed interference test, wherein the observation well is kept closed and the choke of a neighboring signal well is manipulated, with the resulting deviation in the pressure trend being used to determine the well communication. The interference test is not suitable due to requiring wells to be closed and to being applicable to only certain types of wells. Thus, a producer-producer connectivity model is used to analyze well communication and to quantify well communication strength. Such as connectivity model may use production data (not requiring wells be kept closed), does not require the building of a reservoir model, and may consider the simultaneous interaction amongst multiple wells through time.

Description

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application Ser. No. 63/369,001, entitled “METHOD AND SYSTEM FOR MODELING MULTI-WELL COMMUNICATION CONSIDERING MULTIPLE FLOW REGIMES FOR HYDROCARBON MANAGEMENT,” filed Jul. 21, 2022, the disclosure of which is hereby incorporated by reference in its entirety.

FIELD OF THE INVENTION

The present application relates generally to the field of hydrocarbon exploration, development and production. Specifically, the disclosure relates to a methodology for modeling well communication between a plurality of wells considering multiple flow regimes.

BACKGROUND OF THE INVENTION

This section is intended to introduce various aspects of the art, which may be associated with exemplary embodiments of the present disclosure. This discussion is believed to assist in providing a framework to facilitate a better understanding of particular aspects of the present disclosure. Accordingly, it should be understood that this section should be read in this light, and not necessarily as admissions of prior art.

Understanding well communication scenarios in unconventional reservoirs may assist in asset development planning. Inter-well communication between two horizontal wells may be quantified through metrics, such as based on a suitably-designed interference test, whereby an observation well is kept closed (e.g., shut-in) and the choke of a neighboring signal well is manipulated. The resulting deviation in the pressure trend of the observation well may then be estimated via curve fitting.

SUMMARY OF THE INVENTION

In one or some embodiments, a computer-implemented method for determining well communication between a plurality of wells for hydrocarbon management is disclosed. The method includes: accessing a model for determining the well communication between the plurality of wells; determining, using the model and production data, at least one metric indicative of the well communication between the plurality of wells and a plurality of flow regimes between the plurality of wells; and using the at least one metric indicative of the well communication between the plurality of wells over time and the plurality of flow regimes between the plurality of wells over time for hydrocarbon management.

BRIEF DESCRIPTION OF THE DRAWINGS

The present application is further described in the detailed description which follows, in reference to the noted plurality of drawings by way of non-limiting examples of exemplary implementations, in which like reference numerals represent similar parts throughout the several views of the drawings. In this regard, the appended drawings illustrate only exemplary implementations and are therefore not to be considered limiting of scope, for the disclosure may admit to other equally effective embodiments and applications.

FIG. 1A is a first example flow diagram of the disclosed methodology.

FIG. 1B is a second example flow diagram of the disclosed methodology.

FIG. 1C is a third example flow diagram of the disclosed methodology.

FIG. 2 is a first example of a schematic of the gun barrel view of the wells.

FIG. 3A is an illustration of input data.

FIG. 3B is a block diagram of the model with input and output.

FIG. 4A is a second example of a schematic of the gun barrel view of the wells.

FIG. 4B is a graph illustrating time versus the well communication metric.

FIGS. 5A-J illustrate sets of diagnostic plots, with each set including: a regression plot showing the mismatch between raw pressure data and tool-predicted pressure data; and a bar plot showing the communication between a well and its communicating neighbor (with each bar further segmented into different communication scenarios, such as linear, bi-linear, pseudo-steady state), including a first set in FIGS. 5A-B (for well 5), a second set in FIGS. 5C-D (for well 2), a third set in FIGS. 5E-F (for well 1), a fourth set in FIGS. 5G-H (for well 3), and a fifth set in FIGS. 5I-J (for well 6).

FIG. 6 is a diagram of an exemplary computer system that may be utilized to implement the methods described herein.

DETAILED DESCRIPTION OF THE INVENTION

The methods, devices, systems, and other features discussed below may be embodied in a number of different forms. Not all of the depicted components may be required, however, and some implementations may include additional, different, or fewer components from those expressly described in this disclosure. Variations in the arrangement and type of the components may be made without departing from the spirit or scope of the claims as set forth herein. Further, variations in the processes described, including the addition, deletion, or rearranging and order of logical operations, may be made without departing from the spirit or scope of the claims as set forth herein.

It is to be understood that the present disclosure is not limited to particular devices or methods, which may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting. As used herein, the singular forms “a,” “an,” and “the” include singular and plural referents unless the content clearly dictates otherwise. Furthermore, the words “can” and “may” are used throughout this application in a permissive sense (i.e., having the potential to, being able to), not in a mandatory sense (i.e., must). The term “include,” and derivations thereof, mean “including, but not limited to.” The term “coupled” means directly or indirectly connected. The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any aspect described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects. The term “uniform” means substantially equal for each sub-element, within about ±10% variation.

The term “seismic data” as used herein broadly means any data received and/or recorded as part of the seismic surveying and interpretation process, including displacement, velocity and/or acceleration, pressure and/or rotation, wave reflection, and/or refraction data. “Seismic data” is also intended to include any data (e.g., seismic image, migration image, reverse-time migration image, pre-stack image, partially-stack image, full-stack image, post-stack image or seismic attribute image) or interpretation quantities, including geophysical properties such as one or more of: elastic properties (e.g., P and/or S wave velocity, P-Impedance, S-Impedance, density, attenuation, anisotropy and the like); and porosity, permeability or the like, that the ordinarily skilled artisan at the time of this disclosure will recognize may be inferred or otherwise derived from such data received and/or recorded as part of the seismic surveying and interpretation process. Thus, this disclosure may at times refer to “seismic data and/or data derived therefrom,” or equivalently simply to “seismic data.” Both terms are intended to include both measured/recorded seismic data and such derived data, unless the context clearly indicates that only one or the other is intended. “Seismic data” may also include data derived from traditional seismic (e.g., acoustic) data sets in conjunction with other geophysical data, including, for example, gravity plus seismic; gravity plus electromagnetic plus seismic data, etc. For example, joint-inversion utilizes multiple geophysical data types.

The term “geophysical data” as used herein broadly includes seismic data, as well as other data obtained from non-seismic geophysical methods such as electrical resistivity. In this regard, examples of geophysical data include, but are not limited to, seismic data, gravity surveys, magnetic data, electromagnetic data, well logs, image logs, radar data, or temperature data.

The term “geological features” (interchangeably termed geo-features) as used herein broadly includes attributes associated with a subsurface, such as any one, any combination, or all of: subsurface geological structures (e.g., channels, volcanos, salt bodies, geological bodies, geological layers, etc.); boundaries between subsurface geological structures (e.g., a boundary between geological layers or formations, etc.); or structure details about a subsurface formation (e.g., subsurface horizons, subsurface faults, mineral deposits, bright spots, salt welds, distributions or proportions of geological features (e.g., lithotype proportions, facies relationships, distribution of petrophysical properties within a defined depositional facies), etc.). In this regard, geological features may include one or more subsurface features, such as subsurface fluid features, that may be hydrocarbon indicators (e.g., Direct Hydrocarbon Indicator (DHI)).

The terms “velocity model,” “density model,” “physical property model,” or other similar terms as used herein refer to a numerical representation of parameters for subsurface regions. Generally, the numerical representation includes an array of numbers, typically a 2-D or 3-D array, where each number, which may be called a “model parameter,” is a value of velocity, density, or another physical property in a cell, where a subsurface region has been conceptually divided into discrete cells for computational purposes. For example, the spatial distribution of velocity may be modeled using constant-velocity units (layers) through which ray paths obeying Snell's law can be traced. A 3-D geologic model (particularly a model represented in image form) may be represented in volume elements (voxels), in a similar way that a photograph (or 2-D geologic model) may be represented by picture elements (pixels). Such numerical representations may be shape-based or functional forms in addition to, or in lieu of, cell-based numerical representations.

The term “subsurface model” as used herein refer to a numerical, spatial representation of a specified region or properties in the subsurface.

The term “geologic model” as used herein refer to a subsurface model that is aligned with specified geological feature such as faults and specified horizons.

The term “reservoir model” as used herein refer to a geologic model where a plurality of locations have assigned properties including any one, any combination, or all of rock type, EoD, subtypes of EoD (sub-EoD), porosity, clay volume, permeability, fluid saturations, etc.

For the purpose of the present disclosure, subsurface model, geologic model, and reservoir model are used interchangeably unless denoted otherwise.

As used herein, “hydrocarbon management”, “managing hydrocarbons” or “hydrocarbon resource management” includes any one, any combination, or all of the following: hydrocarbon extraction; hydrocarbon production, (e.g., drilling a well and prospecting for, and/or producing, hydrocarbons using the well; and/or, causing a well to be drilled, e.g., to prospect for hydrocarbons); hydrocarbon exploration; identifying potential hydrocarbon-bearing formations; characterizing hydrocarbon-bearing formations; identifying well locations; determining well injection rates; determining well extraction rates; identifying reservoir connectivity; acquiring, disposing of, and/or abandoning hydrocarbon resources; reviewing prior hydrocarbon management decisions; and any other hydrocarbon-related acts or activities, such activities typically taking place with respect to a subsurface formation. The aforementioned broadly include not only the acts themselves (e.g., extraction, production, drilling a well, etc.), but also or instead the direction and/or causation of such acts (e.g., causing hydrocarbons to be extracted, causing hydrocarbons to be produced, causing a well to be drilled, causing the prospecting of hydrocarbons, etc.). Hydrocarbon management may include reservoir surveillance and/or geophysical optimization. For example, reservoir surveillance data may include, well production rates (how much water, oil, or gas is extracted over time), well injection rates (how much water or CO₂ is injected over time), well pressure history, and time-lapse geophysical data. As another example, geophysical optimization may include a variety of methods geared to find an optimum model (and/or a series of models which orbit the optimum model) that is consistent with observed/measured geophysical data and geologic experience, process, and/or observation.

As used herein, “obtaining” data generally refers to any method or combination of methods of acquiring, collecting, or accessing data, including, for example, directly measuring or sensing a physical property, receiving transmitted data, selecting data from a group of physical sensors, identifying data in a data record, and retrieving data from one or more data libraries.

As used herein, terms such as “continual” and “continuous” generally refer to processes which occur repeatedly over time independent of an external trigger to instigate subsequent repetitions. In some instances, continual processes may repeat in real time, having minimal periods of inactivity between repetitions. In some instances, periods of inactivity may be inherent in the continual process.

If there is any conflict in the usages of a word or term in this specification and one or more patent or other documents that may be incorporated herein by reference, the definitions that are consistent with this specification should be adopted for the purposes of understanding this disclosure.

As discussed in the background, one methodology for determining well communication is by shutting down one well and sensing pressure changes generated by a neighboring well. This type of approach has several limitations. First, such a methodology may work well in specific instances, but not generally. For example, when the two wells (e.g., the shut down well and the neighboring well) are communicating through hydraulic fractures (e.g., frac-hit), the response may be observed in a relatively short amount of time. However, other configurations in which there is no frac-hit may necessitate a much longer amount of time to sense the response. Second, such a methodology requires that wells are shut down, thereby delaying production. Third, this type of interference analysis may not be suitable for quantifying communication through certain subsurface environments, such as tight matrix/Stimulated Rock Volume (SRV), in which a production interference signal may propagate more slowly due to the much smaller permeability. Fourth, such pair-wise analysis may not bring a full picture of how multiple wells are interacting with each other in a dense/cube development.

As such, in one or some embodiments, a methodology is disclosed that determines well communication between a plurality of wells (e.g., communication in the form of a measurable pressure disturbance caused by the deliberate production or withdrawal of hydrocarbons and/or injection of fluid by a neighboring well). The methodology includes: accessing a model (such as one or more equations) that may be used to determine well communication between the plurality of wells; determining, using the model and production data, at least one metric indicative of well communication between the plurality of wells for a plurality of flow regimes; and using the at least one metric for hydrocarbon management. Thus, production data may be used in one or more stages of hydrocarbon management, such as during primary depletion and/or during EOR (e.g., during injection of gas into the subsurface and/or annulus of the well).

As discussed in more detail below, the model may be manifested in one of several ways. In one way, the model may comprise one or more equations that includes one or more metrics indicative of well communication amongst the plurality of wells. In practice, the equations may be solved (such as iteratively solved) using production data in order to determine the one or more metrics indicative of well communication amongst the plurality of wells. Production data from one, some or all of the plurality of wells and/or for a plurality of flow regimes are used in order to determine the at least one connectivity metric from the model. Various types of connectivity metrics are contemplated. In one or some embodiments, the connectivity metric may be quantitative in nature (e.g., a numerical value). Alternatively, or in addition, the connectivity metric may be qualitative (e.g., yes/no; strong/medium/low). In either instance, the connectivity metric may provide an indication of the fluid connectivity for the plurality of wells. Further, the nature of the communication, such as the flow regime(s), may likewise be considered. By way of example, any one, any combination, or all of the following flow regimes may be considered: linear flow; bi-linear flow; or pseudo-steady state flow. Moreover, in one or some embodiments, the connectivity metric may be indicative of flow for a specified stage of hydrocarbon management. Merely by way of example, production data during primary depletion may be used to determine the connectivity metric, which may be indicative of flow during the primary depletion stage. Alternatively, or in addition, production data during EOR may be used to determine the connectivity metric, which may be indicative of flow during the EOR (e.g., indicative of flow of gas, injected from an injector well into the subsurface, to one or more neighboring wells, with the connectivity metric being used for management and/or surveillance of gas injection).

The determined connectivity metric may be output in one or more forms, such as in any one, any combination, or all of: a gun barrel plot; bar plots; or the like. The determined connectivity metric may in turn be used for hydrocarbon management in one of several ways. In one way, the determined connectivity metric may assist in one or more phases of hydrocarbon management, such as any one, any combination, or all of: drilling and construction phase; primary depletion phase; or enhanced oil recovery (EOR) stages (e.g., when injecting gas into the reservoir). In particular, the connectivity metric may be used for well spacing decisions. In one example, a very high connectivity metric, as an indicator of a current reservoir interval, may be used to determine that the well spacing for drilling and construction phase in a subsequent reservoir interval may be spaced out further than previously made in the current reservoir interval. Alternatively, or in addition, the connectivity metric may be used as an indicator or characterization of hydraulic fractures (e.g., whether there are overlapping hydraulic fracs, shared stimulated reservoir volume (SRV), or no overlapping). In turn, the connectivity metric may be used to determine whether to alter well completions for hydraulic fracturing for subsequently drilled wells.

In this way, the methodology may comprise a data-driven approach, using a “producer-producer” connectivity model, to analyze the well communication and to quantify the well communication strength (as indicated by the well communication metric). Unlike previously solutions, in one or some embodiments, the methodology has the advantage of considering the simultaneous interaction among/between multiple wells continuously through time without the need of shut-ins of one or more wells or repeated shut-ins to capture communication trends through time. In addition, the methodology may significantly reduce overhead since it does not require engineers to build a reservoir model. Rather, the methodology may be used a stand-alone evaluation tool or as a compliment to traditional reservoir modeling and evaluation (e.g., the methodology may complement existing production surveillance to reconcile trends in producing gas to oil ratio (GOR) with respect to in-bench/cross-bench communication). Moreover, the methodology may be scalable to either flow-back analysis (FBA) or longer-term trends in well communication. Thus, the resultant flows derived from the methodology may be used in one or more stages of hydrocarbon management, such as during any one, any combination, or all of: drilling and construction phase (e.g., in determining the spacing of wells) and/or for fracturing operations (e.g., determining completions for fracturing).

Referring to the figures, FIG. 1A is an example flow diagram 100 of the disclosed methodology. At 110, the data may be input. Various types of data are contemplated and may be read from a plurality of different data sources. As one example, the data source may comprise a direct instrumental measurement device, such as a sensor that directly senses a criterion, such as a sensor that measures flow rate or a sensor that measures pressure (e.g., individual flow meter, test separator for rate measurement and downhole pressure gauge for pressure measurement). As another example, the data source may comprise an indirect measurement device, such as a sensor that indirectly senses the criterion (e.g., virtual metering technique based sensing temperature and pressure at one or more sections of the well, such as sensing at the wellhead and downhole both temperature and pressure). As discussed further below, a virtual sensor may be used to directly sensor one criterion (such as temperature and pressure) and then calculate, based on the sensed measurement, a criterion at issue (such as calculate the flow rate based on the sensed temperature and pressure). Thus, in one embodiment, the data source(s) may comprise a first type of sensor(s) tasked with directly sensing the criteria at issue, such as a flow rate meter to sense flow rate and/or a pressure sensor to sense pressure. Alternatively, the data source(s) may comprise a second type of sensor that acts as a virtual meter(s) that may take measurements, such as temperature and pressure at one or more locations (e.g., at the wellhead and the downhole) and calculate the flow rate. Alternatively, or in addition, production data, indicative of hydrocarbon production from one, some, or all of the wells in a field, may be used as one type of data input.

At 120, the data may be pre-processed. In one embodiment, the pre-processing of the data, while optional, may include any one, any combination, or all of: applying data smoothing, multiphase correction, etc.; defining neighbor lists for one, some or each well; or defining the investigation period. Thus, the pre-processing of the data may be used in order to modify (e.g., clean up) and/or supplement the data (e.g., the production data) prior to analysis. See 130.

At 130, data analysis is performed, such as perform producer-producer analysis. As discussed in more detail below, the analysis may comprise solving one or more metrics, such as at least one metric indicative of the well communication between the plurality of wells, using a model and the production data. The resultant solution may be output including one or both of gun barrel plots indicative of well communication strength or individual curve fitting plots showing dominant features. For example, at 140, a visualization of the at least one metric may be output. An example of the visualization is illustrated in FIG. 4A. At 150, hydrocarbon management may be performed based on the data analysis. By way of example, the determined metric may indicate an amount of communication with neighboring wells, which in turn may be used in the drilling and construction phase (e.g., in determining the spacing of wells) and/or for fracturing operations (e.g., determining completions for fracturing).

Further, in one or some embodiments, the data sources may generate data at a predetermined frequency. For example, in one embodiment, the sensor may generate data daily (e.g., at least once per day; at most once per day) or may generate data multiple times per day (e.g., at least every hour; at least every ½ hour; at least every 15 minutes; at least every 10 minutes; at least every 5 minutes; etc.). Thus, the frequency at which the data is generated may be dependent on the type of instrument (e.g., a virtual meter versus a direct measurement meter) and/or dependent on the phase of production (e.g., a flowback phase versus a production phase). Regardless of the data frequency, the metric for well communication, such as the short-term well communication (e.g., a short-term early well communication hours or days, such as less than 1 week) and/or the long-term well communication (e.g., long-term production interference of at least more than 1 week, such as weeks to months), may be determined. Further, for higher data frequencies (e.g., at least multiple times per day), the determination as to the respective well communication, whether short-term and/or long-term, may be performed much quicker (e.g., in less than 1 day; in less than 6 hours; in less than 3 hours; in less than 1 hour), thereby enabling quicker decisions that are impacted by the determined well communication. This is in contrast to a data frequency of at most once per day, resulting in the determination of well communication taking upwards of at least one week, at least two weeks, at least one month, at least two months, etc.

This impact of data frequency and the determination as to data sufficiency is illustrated in FIGS. 1B-C. In particular, sufficiency of data may be determined in one of several ways. In one way, sufficiency may be based on whether the determined well communication metric has converged (e.g., from one iteration to a previous iteration (or iterations), there is less than a predetermined percent change in the determined well communication metric indicating convergence). This is illustrated, for example, in the flow diagram 160 in FIG. 1B in which flow diagram 160 iterates until data sufficiency is determined at 170, after which flow diagram moves to 140. In another way, sufficiency may be based on a number of iterations (e.g., a predetermined number of iterations, such as 100 iterations, is performed; after which, the determined well communication metric is considered sufficient). This is illustrated, for example, in the flow diagram 180 in FIG. 1C in which flow diagram 180 iterates until data sufficiency is determined at 190, after which flow diagram moves to 120.

FIG. 2 is a first example of a schematic 200 of the gun barrel view of the wells, including well 1 (210), well 2 (220), well 3 (230), well 4 (240), well 5 (250), and well 6 (260). As shown, each respective well may include communication between the respective well and one or more other wells (including flow between adjacent wells). By way of example, well 1 (210) includes communication with well 2 (220), shown as well 1/well 2 communication, communication with well 3 (230), shown as well 1/well 3 communication, and communication with well 4 (240), shown as well 1/well 4 communication. FIG. 2 is merely for illustration purposes. Fewer or greater numbers of wells are contemplated.

FIG. 3A is an illustration 300 of input data. As shown, various types of input data are contemplated including hourly data and/or daily data. Further, the data frequency may be different depending on the well status, such as flowback or production. In particular, with regard to flowback data (hourly), any one, any combination, or all of the following are contemplated: lateral location (e.g., X, Z); neighbor list; record dates (e.g., hourly); oil rates; water rates; gas rates; bottomhole pressure (BHP); or fluid properties including formation volume factors (e.g., for oil (B_o), water (B_w) and/or for gas (B_g)) and/or one or more ratios (e.g., the amount of oil suspended in gas vapor (R_sv) and/or the amount of gas in oil ratio (R_so)). With regard to production data (daily), any one, any combination, or all of the following are contemplated: lateral location (e.g., X, Z); neighbor list; record dates (e.g., daily); oil rates; water rates; gas rates; BHP; or fluid properties including formation volume factors (e.g., for oil (B_o), water (B_w) and/or for gas (B_g)) and/or one or more ratios (e.g., the amount of oil suspended in gas vapor (R_sv) and/or the amount of gas in oil ratio (R_so)).

FIG. 3B is a block diagram of the model 350 with input and output. As illustrated, model 350 may input data, such as the data illustrated in FIG. 3A. Further, the model 350 may be manifested in one of several ways. In one way, the model 350 may be manifested via one or more equations, such as illustrated below. Further, the model 350 may generate an output, such as metric(s) indicative of the well communication between the plurality of wells.

As one example, the model may be manifested in equations, such as illustrated below. In particular, one may assume well k's pressure may be impacted by one, some or all neighboring wells' production rate where the number of wells is represented by i=1, 2, . . . , n_w. The total interference may be described as:

$\begin{array}{cc}\Delta p_k\left(t\right)=p_k\left(t_0\right)-p_k\left(t\right)=\sum _{i=1}^{n_w}\sum _{j=1}^{n_f}{\alpha }_{\mathrm{ij}}{Q}_{\mathrm{ij}}\left(t\right),& \left(1\right)\end{array}$

where Δp_k stands for the pressure drop of well k. Further, as discussed in more detail below, the disclosed methodology may consider multiple flow regimes. This consideration of the multiple flow regimes may be realized in one of several ways. In one way, Q_ij may comprise the feature generated based on well i's rate under the flow regime j (with n_f being the number of flow regimes j. Further, the metric indicative of the well communication between the plurality of wells may be represented in one of several ways. In one way, the metric indicative of the well communication between the plurality of wells may comprise one or more coefficients, such as coefficients (α_ij), which may quantify how strongly (or how weakly) different wells are communicating through various flow regimes. For example, suppose well i is taken from [“Well1”, “Well2”, “Well3”, to “WellN”], flow regime j is taken from a plurality of flow regimes, such as any two or more of [“linear_flow”, “bilinear_flow”, “pseudo_steady_flow”]. Then, α₁₁ may denote contribution from well 1 linear flow regime. Those coefficients (α_ij) may be unknown and may be fitted based on the real production data. As such, in one or some embodiments, the analysis may comprise a data-driven method representing an optimization problem

$\begin{array}{cc}\underset{\alpha }{\min }\begin{array}{c}\left(\Delta p_k\left(t\right)-{\Sigma }_{i=1}^{n_w}{\Sigma }_{j=1}^{n_f}{\alpha }_{\mathrm{ij}}{Q}_{\mathrm{ij}}\left(t\right)+\gamma ❘{\alpha }_{\mathrm{ij}}❘\right)\\ {\alpha }_{\mathrm{ij}}\ge 0\forall i,j.\end{array}.& \left(2\right)\end{array}$

The definition of Q_ij may be based on the theory of rate-transient analysis and superposition in time. Assuming a constant production rate (q) from a horizontal well, if the well is experiencing linear flow regime, the rate-normalized pressure drop is linearly proportional to the square root of time as:

$\begin{array}{cc}\frac{\Delta p\left(t\right)}{q\left(t\right)}\propto t^{\frac{1}{2}}& \left(3\right)\end{array}$

where the coefficient relates the product of fracture geometry and matrix permeability (A√{square root over (K_m)}). If the rate is changing with time, the equation may be changed as:

$\begin{array}{cc}\frac{\Delta p_t}{q_t}\propto {\Sigma }_{i=1}^t\frac{q_i-q_{i-1}}{q_t}*{\left(t_n-t_{i-1}\right)}^{\frac{1}{2}}.& \left(4\right)\end{array}$

Removing the q t may generate the feature Q as:

$\begin{array}{cc}Q\left(n_t\right)={\Sigma }_{s=1}^{n_t}\left[q_i\left(t_s\right)-q_i\left(t_{s-1}\right)\right]*{\left(t_{n_t}-t_{s-1}\right)}^{\frac{1}{2}}.& \left(5\right)\end{array}$

This may represent how a well's pressure is changing if the well is flowing with rate q under linear flow regime. Depending on different flow regimes considered, the functional form of that weighting function

${\left(t-t_{s-1}\right)}^{\frac{1}{2}}$

may vary. The following shows an example summary of considered flow regimes in unconventional applications and the corresponding weighting function.

$\mathrm{Linear}\mathrm{flow}\left(\mathrm{LF}\right){\left(t-t_{s-1}\right)}^{\frac{1}{2}}$ $\mathrm{Bi}-\mathrm{linear}\mathrm{flow}\left(\mathrm{BLF}\right){\left(t-t_{s-1}\right)}^{\frac{1}{4}}$ $\mathrm{Pseudo}-\mathrm{steady}\mathrm{state}\mathrm{flow}\left(\mathrm{PSS}\right){\left(t-t_{s-1}\right)}^1$

Thus, in one or some embodiments, the weighting function is dependent on the identified flow regime. In this way, Q_ij(t) may represent the production rate for well i multiplied by the weighting function for flow regime j. Thus, Q_ij(t) may be used to determine to determine the respective α (e.g., α₁₁ is indicative of linear flow for well 1, α₁₂ is indicative of bi-linear flow for well 1, and α₁₃ is indicative of PSS flow for well 1; α₁ for well 1 may be based on a combination of α11, α₁₂, and α₁₃, such as an average).

Further, with regard to Equation (1), one or more constraints may be imposed including any one, any combination, or all of:

• • (1) Each flow regime's contribution may last throughout an entire time period (e.g., the coefficients capture the average impact of each flow regime across the entire time period). The consideration of the first assumption is that, since each well's production rate generally is not constant throughout the production period, there is no clear transition time to separate contribution from one flow regime to another. Whenever the rate is changing, there may be a new pressure pulse generated at the wellbore and such pressure pulse may experience similar transition of flow regimes (e.g., from linear flow along the fracture to pseudo-steady state across);
  • (2) There is no lag between the pressure interference. In this context, one may assume that the pressure inference occurs instantaneously. This may be under the assumption of superposition in space;
  • (3) The coefficient (a) will have same sign (e.g., all the flow regimes are mutually contributing to the pressure drop of each well).

Thus, in one or some embodiments, equality constraints may be imposed on coefficients α or β, depending on what levels of symmetric contribution are expected. In one or some embodiments, multiple solutions (with multiple flow regimes) may be possible. Further, the data may reflect multiple signals sensed by same receiver. In order to home in on a solution, in one or some embodiments, the analysis focuses on all of the wells together and impose reality-based physical constraints, as illustrated above. Other constraints imposed on the optimization process are contemplated.

By doing so, one may estimate the average impact of a specific well under different flow regimes. In order to avoid over-fitting the problem, a Li regularization (γ|α_ij| may be added in Equation (2) where parameter γ controls how strong the regularization is imposed and the value is tuned through Cross-Validation. In addition, a normalization step may be introduced in order to avoid the optimization process from being impacted by features at different scales. Thus, Equation (1) may be modified to:

$\begin{array}{cc}\Delta p_k\left(t\right)={\Sigma }_{i=1}^{n_w}{\Sigma }_{j=1}^{n_f}{\beta }_{\mathrm{ij}}{\stackrel{\_}{Q}}_{\mathrm{ij}}\left(t\right)={\Sigma }_{i=1}^{n_w}{\Sigma }_{j=1}^{n_f}{\beta }_{\mathrm{ij}}\frac{Q_{\mathrm{ij}}\left(t\right)-Q_{\mathrm{ij},\min }}{Q_{\mathrm{ij},\max }-Q_{\mathrm{ij},\min }},& \left(6\right).\end{array}$

Thus, one, some or all of the wells (see summation for i=1 to n_w) and one, some or each flow regime (see summation for j=1 to n_f) are both considered. Further, the normalized coefficients (β) may be shown as:

β_ij=α_ij*(Q_ij,max−Q_ij,min)  (7).

Once the solution for α (or β) is found, the ratio of the coefficients may be used to characterize the well communication level as:

$\begin{array}{cc}\%\mathrm{of}\mathrm{contribution}\mathrm{Well}B\to A=\frac{{\Sigma }_{j=1}^{N_f}{\alpha }_{\mathrm{Bj}}}{{\Sigma }_{i=1}^{N_w}{\Sigma }_{j=1}^{N_f}{\alpha }_{\mathrm{ij}}},& \left(8\right)\end{array}$

where N_f denotes the number of flow regimes being considered and N_w denotes the number of wells (including the respective well A itself) being models. In one or some embodiments, the user has the option to report either contribution in terms of α or β. In this regard, one or both of α or β may comprise the metric. When considering multiple wells, certain constraints may be imposed to improve the data-driven model quality. Here, one may assume the communication between two wells are symmetric (e.g., the contribution from well A linear flow to well B's pressure drop (e.g., α_A→B,LF) is same as the contribution from well B linear flow to well A's pressure drop (e.g., α_B→A,LF), so that α_A→B,LF=α_B→A,LF. Here, one need not impose that the actual contribution to the pressure loss is the same since each well is not producing at the same rate or schedule. The data-driven model may become a constrained optimization where the objective function is defined as:

$\begin{array}{cc}\mathrm{obj}={\Sigma }_{i=1}^{n_{\mathrm{well}}}\Delta p_{i,\mathrm{raw}}-\Delta p_{i,\mathrm{model}}+\gamma ❘\alpha ❘={\Sigma }_{i=1}^{n_{\mathrm{well}}}\Delta p_{i,\mathrm{raw}}-F_i*{\alpha }_i+\gamma ❘\alpha ❘& \left(9\right)\end{array}$ $s.t.{\alpha }_i={\alpha }_j\forall i,j\mathrm{in}\mathrm{coefficient}\mathrm{list}.$

In Equation (9), the objective function may seek to minimize the misfit between actual (e.g., “raw”) and modeled pressures where γ may comprise a numerical parameter to help convergence.

Thus, as shown above, the methodology comprises a simultaneous assessment of the contribution from two or more wells, such as at least three wells, at least four wells, or at least all of the wells. Further, in one or some embodiments, the methodology may assess flow in a time dependent manner, and not be limited to specific periods (e.g., flowback). Rather, the methodology may comprise a periodic or a continuous assessment (e.g., based on received production data and/or pressure data) of the entirety of the wells.

FIG. 4A is a second example of a schematic of the gun barrel view 400 of the wells. Specifically, the gun barrel view 400 illustrates the contribution of the respective well to itself (within the circle), and contributions from neighboring wells. For example, with regard to well 4, well 4 contributes 85.3% to itself, with well 1, well 2, well 3, and well 6 contributing 0.4%, 1.5%, 12%, and 0.9%, respectively. In one or some embodiments, the percentages indicated in the gun barrel view 400 may comprise normalized a, as illustrated in the equations above. Thus, the numbers shown in the gun barrel view 400 are the percentage of communication but not the actual alpha coefficient. In this regard, in one embodiment, the communication between a respective two wells may not be identical (e.g., different percentages) and may slightly differ due to the normalization. Alternatively, the generated output (e.g., the gun barrel view) may illustrate identical communication between respective wells (e.g., where normalization is not performed). In this regard, the generated output may either indicate identical or different communication between respective wells depending on the mathematical operations performed. It is noted that the gun barrel view 400 is merely one example of illustrating the metric indicative of well communication between the plurality of wells. It is further noted that an output need not be generated. In particular, in one or some embodiments, the output of the model (such as model 350) may be automatically input to another system, such as a machine learning (ML) functionality, which in turn may be used for further analysis and/or hydrocarbon management.

FIG. 4B is a graph 450 illustrating time versus values of the well communication metric (α). As discussed above, the well communication metric (e.g., α) may vary over time. This is unlike typical solutions, which do not consider the potential time-varying nature of well communication. Data may be obtained to illustrate well communication at a particular point in time. This is illustrated, for example, in FIGS. 5B, 5D, 5F, 5H, and 5J (e.g., FIG. 5B is a bar-graph illustration well communication for well 5 for a specific time). In order to understand the well communication metric as it varies over time, the data may be plotted versus time, thereby providing an illustration of the varying communication metric across the different snapshots of time. FIG. 4B illustrates such an example, whereby the different curves 452, 454, 456, 458, 460, each of which may correlate to the well communication metric over time (e.g., curve 452 may correlate to well 1's communication with itself, curve 454 may correlate to well 2's communication with well 1, curve 456 may correlate to well 3's communication with well 1, curve 458 may correlate to well 4's communication with well 1, curve 460 may correlate to well 5's communication with well 1). The time-varying illustration of the well communication metric may be used for hydrocarbon management, such as modifying the management of hydrocarbon extraction and/or fluid injection in a neighboring well.

FIGS. 5A-J illustrate sets of diagnostic plots, with each set including: a regression plot showing the mismatch between raw pressure data and tool-predicted pressure data; and a bar plot showing the communication between a well and its communicating neighbor (with each bar further segmented into different communication scenarios, such as linear, bi-linear, pseudo-steady state), including a first set in FIGS. 5A-B, a second set in FIGS. 5C-D, a third set in FIGS. 5E-F, a fourth set in FIGS. 5G-H, and a fifth set in FIGS. 5I-J. As discussed above, FIGS. 5B, 5D, 5F, 5H and 5J illustrate well communication at a particular point in time.

In particular, FIG. 5A illustrates a graph 500 for well 5 (and communication with respect to well 3, well 5, and well 6) illustrating raw pressure 510 and prediction from the workflow (e.g., the disclosed methodology) 512, with an error of 1.76%. FIG. 5B is a bar plot 520 showing the communication between well 5 and itself, and neighbors well 3 and well 6 (reflecting the contributions in FIG. 4A), with each bar broken down into the different communication scenarios of linear flow 530, pseudo-steady state flow 532, and bi-linear flow 534.

FIG. 5C illustrates a graph 540 for well 2 (and communication with respect to well 2, well 3, and well 4) illustrating raw pressure 510 and prediction from the workflow (e.g., the disclosed methodology) 512, with an error of 2.63%. FIG. 5D is a bar plot 545 showing the communication between well 2 and itself, and neighbors well 3 and well 4 (reflecting the contributions in FIG. 4A), with each bar broken down into the different communication scenarios of linear flow 530, pseudo-steady state flow 532, and bi-linear flow 534.

FIG. 5E illustrates a graph 550 for well 1 (and communication with respect to well 1, well 2, and well 4) illustrating raw pressure 510 and prediction from the workflow (e.g., the disclosed methodology) 512, with an error of 2.24%. FIG. 5F is a bar plot 555 showing the communication between well 1 and itself, and neighbors well 2 and well 4 (reflecting the contributions in FIG. 4A), with each bar broken down into the different communication scenarios of linear flow 530, pseudo-steady state flow 532, and bi-linear flow 534.

FIG. 5G illustrates a graph 560 for well 3 (and communication with respect to well 2, well 3, well 4, well 5, and well 6) illustrating raw pressure 510 and prediction from the workflow (e.g., the disclosed methodology) 512, with an error of 2.16%. FIG. 5H is a bar plot 565 showing the communication between well 3 and itself, and neighbors well 2, well 4, well 5, and well 6 (reflecting the contributions in FIG. 4A) with each bar broken down into the different communication scenarios of linear flow 530, pseudo-steady state flow 532, and bi-linear flow 534.

FIG. 5I illustrates a graph 570 for well 6 (and communication with respect to well 3, well 4, well 5, and well 6) illustrating raw pressure 510 and prediction from the workflow (e.g., the disclosed methodology) 512, with an error of 1.22%. FIG. 5J is a bar plot 575 showing the communication between well 6 and itself, and neighbors well 3, well 4, and well 5 (reflecting the contributions in FIG. 4A) with each bar broken down into the different communication scenarios of linear flow 530, pseudo-steady state flow 532, and bi-linear flow 534.

In all practical applications, the present technological advancement must be used in conjunction with a computer, programmed in accordance with the disclosures herein. For example, FIG. 6 is a diagram of an exemplary computer system 600 that may be utilized to implement methods described herein. A central processing unit (CPU) 602 is coupled to system bus 604. The CPU 602 may be any general-purpose CPU, although other types of architectures of CPU 602 (or other components of exemplary computer system 600) may be used as long as CPU 602 (and other components of computer system 600) supports the operations as described herein. Those of ordinary skill in the art will appreciate that, while only a single CPU 602 is shown in FIG. 6, additional CPUs may be present. Moreover, the computer system 600 may comprise a networked, multi-processor computer system that may include a hybrid parallel CPU/GPU system. The CPU 602 may execute the various logical instructions according to various teachings disclosed herein. For example, the CPU 602 may execute machine-level instructions for performing processing according to the operational flow described.

The computer system 600 may also include computer components such as non-transitory, computer-readable media. Examples of computer-readable media include computer-readable non-transitory storage media, such as a random-access memory (RAM) 606, which may be SRAM, DRAM, SDRAM, or the like. The computer system 600 may also include additional non-transitory, computer-readable storage media such as a read-only memory (ROM) 608, which may be PROM, EPROM, EEPROM, or the like. RAM 606 and ROM 608 hold user and system data and programs, as is known in the art. The computer system 600 may also include an input/output (I/O) adapter 610, a graphics processing unit (GPU) 614, a communications adapter 622, a user interface adapter 624, a display driver 616, and a display adapter 618.

The I/O adapter 610 may connect additional non-transitory, computer-readable media such as storage device(s) 612, including, for example, a hard drive, a compact disc (CD) drive, a floppy disk drive, a tape drive, and the like to computer system 600. The storage device(s) may be used when RAM 606 is insufficient for the memory requirements associated with storing data for operations of the present techniques. The data storage of the computer system 600 may be used for storing information and/or other data used or generated as disclosed herein. For example, storage device(s) 612 may be used to store configuration information or additional plug-ins in accordance with the present techniques. Further, user interface adapter 624 couples user input devices, such as a keyboard 628, a pointing device 626 and/or output devices to the computer system 600. The display adapter 618 is driven by the CPU 602 to control the display on a display device 620 to, for example, present information to the user such as subsurface images generated according to methods described herein.

The architecture of computer system 600 may be varied as desired. For example, any suitable processor-based device may be used, including without limitation personal computers, laptop computers, computer workstations, and multi-processor servers. Moreover, the present technological advancement may be implemented on application specific integrated circuits (ASICs) or very large scale integrated (VLSI) circuits. In fact, persons of ordinary skill in the art may use any number of suitable hardware structures capable of executing logical operations according to the present technological advancement. The term “processing circuit” encompasses a hardware processor (such as those found in the hardware devices noted above), ASICs, and VLSI circuits. Input data to the computer system 600 may include various plug-ins and library files. Input data may additionally include configuration information.

Preferably, the computer is a high-performance computer (HPC), known to those skilled in the art. Such high-performance computers typically involve clusters of nodes, each node having multiple CPU's and computer memory that allow parallel computation. The models may be visualized and edited using any interactive visualization programs and associated hardware, such as monitors and projectors. The architecture of system may vary and may be composed of any number of suitable hardware structures capable of executing logical operations and displaying the output according to the present technological advancement. Those of ordinary skill in the art are aware of suitable supercomputers available from Cray or IBM or other cloud computing based vendors such as Microsoft. Amazon.

The above-described techniques, and/or systems implementing such techniques, can further include hydrocarbon management based at least in part upon the above techniques, including using the device in one or more aspects of hydrocarbon management. For instance, methods according to various embodiments may include managing hydrocarbons based at least in part upon the device and data representations constructed according to the above-described methods. In particular, such methods may use the device to evaluate various welds in the context of drilling a well.

It is intended that the foregoing detailed description be understood as an illustration of selected forms that the invention can take and not as a definition of the invention. It is only the following claims, including all equivalents which are intended to define the scope of the claimed invention. Further, it should be noted that any aspect of any of the preferred embodiments described herein may be used alone or in combination with one another. Finally, persons skilled in the art will readily recognize that in preferred implementation, some, or all of the steps in the disclosed method are performed using a computer so that the methodology is computer implemented. In such cases, the resulting physical properties model may be downloaded or saved to computer storage.

The following example embodiments of the invention are also disclosed.

Embodiment 1: A computer-implemented method for determining well communication between a plurality of wells for hydrocarbon management, the method comprising:

• • accessing a model for determining the well communication between the plurality of wells;
  • determining, using the model and production data, at least one metric indicative of the well communication between the plurality of wells and a plurality of flow regimes between the plurality of wells; and
  • using the at least one metric indicative of the well communication between the plurality of wells over time and the plurality of flow regimes between the plurality of wells over time for hydrocarbon management.

Embodiment 2: The method of embodiment 1:

• • wherein the plurality of flow regimes comprises linear, bi-linear, and pseudo-steady state.

Embodiment 3: The method of embodiments 1 or 2:

• • wherein the at least one metric of communication comprises a plurality of coefficients indicative of flow for each of the linear, bi-linear, and pseudo-steady state flow regimes.

Embodiment 4: The method of any of embodiments 1-3:

• • wherein the plurality of coefficients are normalized across the plurality of flow regimes.

Embodiment 5: The method of any of embodiments 1-4:

• • wherein the plurality of coefficients are determined based on the production data in the plurality of wells and pressure drops between the plurality of wells.

Embodiment 6: The method of any of embodiments 1-5:

• • wherein the production data is obtained without shutting down of any of the plurality of wells.

Embodiment 7: The method of any of embodiments 1-6:

• • wherein data is obtained from at least one of:
  • direct instrumental measurement comprising at least one of an individual flow meter, a test separator for test measurement, or a downhole pressure gauge for pressure measurement; or
  • an indirect measurement comprising a virtual metering technique based on sensing temperature and pressure at one or more sections of the well.

Embodiment 8: The method of any of embodiments 1-7:

• • wherein data frequency of the data is dependent on one or both of a phase of hydrocarbon management or a type of data source.

Embodiment 9: The method of any of embodiments 1-8:

• • wherein depending on the data frequency, the at least one metric indicative of the well communication determined comprises a short-term early well communication of no more than 1 week or a long-term production interference of at least more than one week.

Embodiment 10: The method of any of embodiments 1-9:

• • wherein determining the at least one metric indicative of the well communication between the plurality of wells and the plurality of flow regimes between the plurality of wells is based on imposing one or more equality constraints on the at least one metric as part of an optimization process.

Embodiment 11: The method of any of embodiments 1-10:

• • wherein the one or more equality constraints comprise contribution from a respective well's linear flow to an adjacent well's pressure drop is equal to the contribution from the adjacent well's linear flow to the respective well's pressure drop.

Embodiment 12: The method of any of embodiments 1-11:

• • wherein the one or more equality constraints does not include actual contribution to pressure loss being the same for the respective well and the adjacent well.

Embodiment 13: The method of any of embodiments 1-12:

• • wherein the one or more equality constraints comprise each respective flow regime's contribution lasting throughout an entire designated time period thereby capturing an average impact of each respective flow regime across the entire designated time period.

Embodiment 14: The method of any of embodiments 1-13:

• • wherein the at least one metric indicative of the well communication between the plurality of wells and a plurality of flow regimes between the plurality of wells is time dependent.

Embodiment 15: The method of any of embodiments 1-14:

• • further comprising generating a gun barrel plot indicative for a respective well contributions to flow of the respective well and its neighboring wells.

Embodiment 16: The method of any of embodiments 1-15:

• • wherein the production data is during a primary depletion stage of hydrocarbon management.

Embodiment 17: The method of any of embodiments 1-16:

• • wherein the production data is during an enhanced oil recovery (EOR) stage of hydrocarbon management in which gas is injected into a reservoir.

Embodiment 18: The method of any of embodiments 1-17:

• • wherein using the at least one metric indicative of the well communication between the plurality of wells over time and the plurality of flow regimes between the plurality of wells over time for hydrocarbon management comprises determining spacing of wells based on the at least one metric.

Embodiment 19: The method of any of embodiments 1-18:

• • wherein using the at least one metric indicative of the well communication between the plurality of wells over time and the plurality of flow regimes between the plurality of wells over time for hydrocarbon management comprises configuring completions of wells based on the at least one metric.

Embodiment 20: The method of any of embodiments 1-19:

• • wherein the model is based on a pressure drop between wells for a plurality of flow regimes and based on flow rate between the wells.

Embodiment 21: The method of any of embodiments 1-20:

• • wherein the model comprises:

$\sum _{i=1}^{n_w}\sum _{j=1}^{n_f}{\alpha }_{\mathrm{ij}}{Q}_{\mathrm{ij}}\left(t\right)$

• • wherein n_w is a number of wells, of is a number of flow regimes, Q_ij is a feature generated based on well i's rate under flow regime j, and wherein α_ij is indicative of well communication for well i under the flow regime j; and
  • wherein determining the at least one metric comprises determining α_ij by data fitting the model based on production data.

Embodiment 22

The method of any of embodiments 1-21:

• • wherein Q_ij is determined based on different weighting functions for each of the flow regimes.

Embodiment 23: The method of any of embodiments 1-22:

• • wherein the at least one metric for a respective well a, comprises a combination of α_ij for each of the flow regime j.

Embodiment 24: A system comprising:

• • a processor; and
  • a non-transitory machine-readable medium comprising instructions that, when executed by the processor, cause a computing system to perform a method according to any of embodiments 1-23.

Embodiment 25: A non-transitory machine-readable medium comprising instructions that, when executed by a processor, cause a computing system to perform a method according to any of embodiments 1-23.

Claims

1. A computer-implemented method for determining well communication between a plurality of wells for hydrocarbon management, the method comprising:

accessing a model for determining the well communication between the plurality of wells;

determining, using the model and production data, at least one metric indicative of the well communication between the plurality of wells and a plurality of flow regimes between the plurality of wells; and

using the at least one metric indicative of the well communication between the plurality of wells over time and the plurality of flow regimes between the plurality of wells over time for hydrocarbon management.

2. The method of claim 1, wherein the plurality of flow regimes comprises linear, bi-linear, and pseudo-steady state.

3. The method of claim 2, wherein the at least one metric of communication comprises a plurality of coefficients indicative of flow for each of the linear, bi-linear, and pseudo-steady state flow regimes.

4. The method of claim 3, wherein the plurality of coefficients are normalized across the plurality of flow regimes.

5. The method of claim 3, wherein the plurality of coefficients are determined based on the production data in the plurality of wells and pressure drops between the plurality of wells.

6. The method of claim 1, wherein the production data is obtained without shutting down of any of the plurality of wells.

7. The method of claim 1, wherein data is obtained from at least one of:

direct instrumental measurement comprising at least one of an individual flow meter, a test separator for test measurement, or a downhole pressure gauge for pressure measurement; or

an indirect measurement comprising a virtual metering technique based on sensing temperature and pressure at one or more sections of the well.

8. The method of claim 7, wherein data frequency of the data is dependent on one or both of a phase of hydrocarbon management or a type of data source.

9. The method of claim 8, wherein depending on the data frequency, the at least one metric indicative of the well communication determined comprises a short-term early well communication of no more than 1 week or a long-term production interference of at least more than one week.

10. The method of claim 1, wherein determining the at least one metric indicative of the well communication between the plurality of wells and the plurality of flow regimes between the plurality of wells is based on imposing one or more equality constraints on the at least one metric as part of an optimization process.

11. The method of claim 10, wherein the one or more equality constraints comprise contribution from a respective well's linear flow to an adjacent well's pressure drop is equal to the contribution from the adjacent well's linear flow to the respective well's pressure drop.

12. The method of claim 11, wherein the one or more equality constraints does not include actual contribution to pressure loss being the same for the respective well and the adjacent well.

13. The method of claim 11, wherein the one or more equality constraints comprise each respective flow regime's contribution lasting throughout an entire designated time period thereby capturing an average impact of each respective flow regime across the entire designated time period.

14. The method of claim 1, wherein the at least one metric indicative of the well communication between the plurality of wells and a plurality of flow regimes between the plurality of wells is time dependent.

15. The method of claim 1, further comprising generating a gun barrel plot indicative for a respective well contributions to flow of the respective well and its neighboring wells.

16. The method of claim 1, wherein the production data is during a primary depletion stage of hydrocarbon management.

17. The method of claim 1, wherein the production data is during an enhanced oil recovery (EOR) stage of hydrocarbon management in which gas is injected into a reservoir.

18. The method of claim 1, wherein using the at least one metric indicative of the well communication between the plurality of wells over time and the plurality of flow regimes between the plurality of wells over time for hydrocarbon management comprises determining spacing of wells based on the at least one metric.

19. The method of claim 1, wherein using the at least one metric indicative of the well communication between the plurality of wells over time and the plurality of flow regimes between the plurality of wells over time for hydrocarbon management comprises configuring completions of wells based on the at least one metric.

20. The method of claim 1, wherein the model is based on a pressure drop between wells for a plurality of flow regimes and based on flow rate between the wells.

21. The method of claim 20, wherein the model comprises: ∑ i = 1 n w ∑ j = 1 n f α ij ⁢ Q ij ( t )

wherein nw is a number of wells, nf is a number of flow regimes, Qij is a feature generated based on well i's rate under flow regime j, and wherein αij is indicative of well communication for well i under the flow regime j; and wherein determining the at least one metric comprises determining αij by data fitting the model based on production data.

22. The method of claim 21, wherein Qij is determined based on different weighting functions for each of the flow regimes.

23. The method of claim 22, wherein the at least one metric for a respective well αi comprises a combination of αij for each of the flow regime j.