AUTONOMOUS DESIGN AND PLACEMENT OF HYDROCARBON WELLS IN NUMERICAL RESERVOIR SIMULATOR

Abstract

A method to perform drilling operations in a field is disclosed. The method includes performing, through simulation time-steps corresponding to time points in a multi-year duration, a reservoir simulation based on a reservoir model of the field, generating, in response to determining a new well is to be drilled during a simulation time-step, a sweet spot map for drilling the new well, the sweet spot map excluding existing well locations in the reservoir model at the simulation time-step, generating, based on the sweet spot map, a well trajectory of the new well for adding to the reservoir model, generating, based at least on the well trajectory of the new well added to the reservoir model, a simulation result of the reservoir simulation, and drilling, based on the simulation result and at the time point of the multi-year duration, the new well according to the well trajectory in the field.

Description

BACKGROUND

In field development planning at the field scale level, the process of well placement requires significant amount of knowledge regarding the desired hydrocarbon reservoir and experience in assisted software applications. One bottleneck in the entire process is the need to run numerical reservoir simulations to evaluate the newly designed and placed wells. Running many simulations throughout the optimization process drastically increases the overall turnaround time.

SUMMARY

In general, in one aspect, the invention relates to a method to perform drilling operations in a field. The method includes obtaining a field development plan of the field, the field development plan comprising a new well drilling schedule for a multi-year duration, performing, through a sequence of simulation time-steps corresponding to a sequence of time points in the multi-year duration, a reservoir simulation based on a reservoir model of the field, determining, during a first simulation time-step of the reservoir simulation, that the new well drilling schedule comprises drilling a first new well at a first time point corresponding to the first simulation time-step, generating, in response to said determining, a first sweet spot map for drilling the first new well, the first sweet spot map excludes a first plurality of existing well locations in the reservoir model at the first simulation time-step, generating, based on the first sweet spot map, a first well trajectory of the first new well for adding to the reservoir model, generating, based at least on the first well trajectory of the first new well added to the reservoir model, a first simulation result of the reservoir simulation, and drilling, based at least on the first simulation result and at the first time point of the multi-year duration, the first new well according to the first well trajectory in the field.

In general, in one aspect, the invention relates to a well design and placement analyzer to facilitate drilling operations in a field. The well design and placement analyzer includes a computer processor and memory storing instructions, when executed by the computer processor comprising functionality for obtaining a field development plan of the field, the field development plan comprising a new well drilling schedule for a multi-year duration, wherein a reservoir simulation is performed through a sequence of simulation time-steps corresponding to a sequence of time points in the multi-year duration based on a reservoir model of the field, and wherein the new well drilling schedule comprises drilling a first new well at a first time point corresponding to a first simulation time-step of the sequence of simulation time-steps, generating, during the first simulation time-step, a first sweet spot map for drilling the first new well, the first sweet spot map excludes a first plurality of existing well locations in the reservoir model at the first simulation time-step, and generating, based on the first sweet spot map, a first well trajectory of the first new well for adding to the reservoir model, wherein a first simulation result of the reservoir simulation is generated based at least on the first well trajectory of the first new well added to the reservoir model, and wherein the first new well is drilled, based at least on the first simulation result and at the first time point of the multi-year duration, according to the first well trajectory in the field.

In general, in one aspect, the invention relates to a system that includes a wellsite for performing drilling operations in a field, a reservoir simulator comprising the functionality for obtaining a field development plan of the field, the field development plan comprising a new well drilling schedule for a multi-year duration, performing, through a sequence of simulation time-steps corresponding to a sequence of time points in the multi-year duration, a reservoir simulation based on a reservoir model of the field, and determining, during a first simulation time-step of the reservoir simulation, that the new well drilling schedule comprises drilling a first new well at a first time point corresponding to the first simulation time-step, and a well design and placement analyzer comprising the functionality for generating, in response to said determining, a first sweet spot map for drilling the first new well, the first sweet spot map excludes a first plurality of existing well locations in the reservoir model at the first simulation time-step, and generating, based on the first sweet spot map, a first well trajectory of the first new well for adding to the reservoir model, wherein the reservoir simulator further comprising the functionality for generating, based at least on the first well trajectory of the first new well added to the reservoir model, a first simulation result of the reservoir simulation, and wherein the first new well is drilled, based at least on the first simulation result and at the first time point of the multi-year duration, at the wellsite according to the first well trajectory.

Other aspects and advantages will be apparent from the following description and the appended claims.

BRIEF DESCRIPTION OF DRAWINGS

Specific embodiments of the disclosed technology will now be described in detail with reference to the accompanying figures. Like elements in the various figures are denoted by like reference numerals for consistency.

FIG. 1 shows a system in accordance with one or more embodiments.

FIG. 2 shows a flowchart in accordance with one or more embodiments.

FIGS. 3A-3I show an example in accordance with one or more embodiments.

FIG. 4 shows a computing system in accordance with one or more embodiments.

DETAILED DESCRIPTION

In the following detailed description of embodiments of the disclosure, numerous specific details are set forth in order to provide a more thorough understanding of the disclosure. However, it will be apparent to one of ordinary skill in the art that the disclosure may be practiced without these specific details. In other instances, well-known features have not been described in detail to avoid unnecessarily complicating the description.

Throughout the application, ordinal numbers (e.g., first, second, third, etc.) may be used as an adjective for an element (i.e., any noun in the application). The use of ordinal numbers is not to imply or create any particular ordering of the elements nor to limit any element to being only a single element unless expressly disclosed, such as using the terms “before”, “after”, “single”, and other such terminology. Rather, the use of ordinal numbers is to distinguish between the elements. By way of an example, a first element is distinct from a second element, and the first element may encompass more than one element and succeed (or precede) the second element in an ordering of elements.

Embodiments of this disclosure provide a method and system for well placement optimization in an oil and gas field. The well placement optimization includes identifying optimum locations of new hydrocarbon wells and determining well trajectories using a numerical reservoir simulator. Accordingly, an optimum field development plan is generated by performing minimum number of numerical reservoir simulations. Based on the optimum field development plan, new hydrocarbon wells are drilled in the field with a significant reduction in the consumption of human resources and hardware computation resources to improve efficiency and time to production of the field.

In one or more embodiments of the invention, the well placement optimization is a workflow performed concurrently with the numerical reservoir simulation. In particular, the well placement optimization is based on time-varying sweet spot maps that are iteratively calculated throughout the numerical reservoir simulation. Further, optimum locations of new wells are identified using clustering analysis on time-varying sweet spot maps. Corresponding well trajectories are identified by applying data regression to the identified sweet spot clusters.

FIG. 1 shows a schematic diagram in accordance with one or more embodiments. As shown in FIG. 1, a field (100) includes a subterranean formation (“formation”) (104) and a well system (106). The formation (104) may include a porous or fractured rock formation that resides underground, beneath the earth's surface (“surface”) (108). The formation (104) may include different layers of rock having varying characteristics, such as varying degrees of permeability, porosity, capillary pressure, and resistivity. In the case of the well system (106) being a hydrocarbon well, the formation (104) may include a hydrocarbon-bearing reservoir (102). In the case of the well system (106) being operated as a production well, the well system (106) may facilitate the extraction of hydrocarbons (or “production”) from the reservoir (102). Although only a single well system (106) is shown in FIG. 1, many well systems may exist throughout the field (100) with new wells to be drilled over a multi-year duration according to a field development plan.

In some embodiments disclosed herein, the well system (106) includes a rig (101), a wellbore (120) with a casing (121), and a well control system (126) located at the wellsite (100a). The well control system (126) may control various operations of the well system (106), such as well production operations, well drilling operation, well completion operations, well maintenance operations, and reservoir monitoring, assessment and development operations. In one or more embodiments, the well control system (126) performs these functionalities using the method described in reference to FIG. 2 below. In some embodiments, the well control system (126) includes a computer system, such as a portion of the computing system described in reference to FIG. 4 below.

The rig (101) is the machine used to drill a borehole to form the wellbore (120). Major components of the rig (101) include the drilling fluid tanks, the drilling fluid pumps (e.g., rig mixing pumps), the derrick or mast, the draw works, the rotary table or top drive, the drill string, the power generation equipment and auxiliary equipment. Drilling fluid, also referred to as “drilling mud” or simply “mud,” is used to facilitate drilling boreholes into the earth, such as drilling oil and natural gas wells. The main functions of drilling fluids include providing hydrostatic pressure to prevent formation fluids from entering into the borehole, keeping the drill bit cool and clean during drilling, carrying out drill cuttings, and suspending the drill cuttings while drilling is paused and when the drilling assembly is brought in and out of the borehole.

The wellbore (120) includes a bored hole (i.e., borehole) that extends from the surface (108) towards a target zone of the formation (104), such as the reservoir (102). An upper end of the wellbore (120), terminating at or near the surface (108), may be referred to as the “up-hole” end of the wellbore (120), and a lower end of the wellbore, terminating in the formation (104), may be referred to as the “downhole” end of the wellbore (120). The wellbore (120) may facilitate the circulation of drilling fluids during drilling operations for the wellbore (120) to extend towards the target zone of the formation (104) (e.g., the reservoir (102)), facilitate the flow of hydrocarbon production (e.g., oil and gas) from the reservoir (102) to the surface (108) during production operations, facilitate the injection of substances (e.g., water) into the hydrocarbon-bearing formation (104) or the reservoir (102) during injection operations, or facilitate the communication of monitoring devices (e.g., logging tools) lowered into the formation (104) or the reservoir (102) during monitoring operations (e.g., during in situ logging operations).

In some embodiments, the well system (106) is provided with a bottom hole assembly (BHA) (151) attached to the drill string (150) to suspend into the wellbore (120) for performing the well drilling operation. The bottom hole assembly (BHA) is the lowest part of a drill string and includes the drill bit, drill collar, stabilizer, mud motor, etc.

In some embodiments, the well system (106) includes a reservoir simulator (160). For example, the reservoir simulator (160) may include hardware and/or software with functionality for generating one or more reservoir models regarding the hydrocarbon-bearing formation (104) and/or performing one or more reservoir simulations. For example, the reservoir simulator (160) may store well logs and data regarding reservoir samples for performing simulations. While the reservoir simulator (160) is shown at a well site, embodiments are contemplated where reservoir simulators are located away from well sites. In some embodiments, the reservoir simulator (160) may include a computer system that is similar to the computer system (400) described below with regard to FIG. 4 and the accompanying description.

In a typical reservoir simulation performed using the reservoir simulator (160), a mathematical model of the reservoir includes a set of partial differential equations representing reservoir and well flows that are solved numerically. Numerical solution involves time and space/domain discretization replacing differential equations with difference equations. Time discretization refers to division of time into a sequence of simulation time-steps. For a typical reservoir simulation, each simulation time-step may represent one day, one week, etc., during the actual production operation that corresponds to seconds or minutes in simulation run time executed by the computer. In each simulation time-step, after discretization is solved iteratively, a non-linear system is linearized using Newton method, which may take several Newton iterations to converge. Space/domain discretization, also called grid generation, refers to division of the reservoir domain into a reservoir grid of small grid blocks. A grid is a tessellation of a set of contiguous polygonal (2D) or polyhedral (3D) objects referred to as grid blocks/cells/elements/control volumes. The reservoir model used by the well design and placement analyzer (161) and reservoir simulator (160) represents the reservoir (102) based on a grid with corresponding petrophysical data.

In some embodiments, the well system (106) includes a well design and placement analyzer (161). For example, the well design and placement analyzer (161) may include hardware and/or software with functionality for identifying optimum locations of new hydrocarbon wells and determining well trajectories using the reservoir simulator (160). In one or more embodiments, the well design and placement analyzer (161) performs the well placement optimization concurrently with numerical reservoir simulations performed by the reservoir simulator (160). In particular, the well placement optimization is based on time-varying sweet spot maps that are iteratively calculated throughout the numerical reservoir simulation. The sweet spot map is a two-dimensional (2D) or three-dimensional (3D) map that identifies most effective locations for placing a new well. Further, optimum locations of new wells are identified using clustering analysis on time-varying sweet spot maps. Corresponding well trajectories are identified by applying data regression to the identified sweet spot clusters. While the well design and placement analyzer (161) is shown at a wellsite, embodiments are contemplated where the well design and placement analyzer (161) is located away from wellsites. In some embodiments, the well design and placement analyzer (161) may include a computer system that is similar to the computer system (400) described below with regard to FIG. 4 and the accompanying description.

Field development plans involve placing wells at different phases (months or years) throughout a multi-year time duration. Hundreds of wells are planned and then scheduled to be drilled throughout a specific range of years. Therefore, at each phase, a set of new wells are drilled. The drilling of the new wells targets the best locations at the time of the current phase. In other words, the sweet spot maps are evaluated using the well design and placement analyzer (161) at the time of the current phase to identify the best locations to place the new wells. In one or more embodiments, the entire field development plan across the multi-year time duration is simulated by performing one single numerical simulation run. This numerical simulation run outputs the new wells that are optimally designed and placed at each phase of the multi-year duration. The numerical simulation output includes new well path trajectory points as well as predicted production performance.

Performing this optimization as part of the numerical simulation run adds a negligible amount to the overall simulation runtime. At each simulation time-step, if there are wells requested to be designed and placed, the reservoir simulator (160) invokes the well design and placement analyzer (161) to run the optimization process and then wait for the result before it continues the simulation.

The well design and placement analyzer (161) includes a well placement engine and a well design engine that collectively perform the well placement optimization process in two parts. The first part performed by the well placement engine is to identify the location of each new well using a clustering analysis algorithm. The location involves identifying a volume of the reservoir with acceptable producer quality to be allocated for one well. Specifically, a volume having a producer quality measure exceeding a pre-determined threshold is included in a 3D sweet spot map. For example, the 3D sweet spot map may be calculated based on the Reservoir Opportunity Index (ROI) as the pre-determined quality measure. ROI is a spatial measure of productivity potential for a reservoir, such as given be Eq. (1) below.

$\begin{array}{cc}\mathrm{ROI}=\sqrt[3]{\mathrm{RQI}\times \mathrm{SOMPV}\times P},& \left(1\right)\end{array}$

where, in turn, the Reservoir Quality Index (RQI) is given by

$\begin{array}{cc}\mathrm{RQI}=0.0314\times \sqrt{k/\varphi },& \left(2\right)\end{array}$

with k being the absolute rock permeability, ϕ the rock porosity, and the SOMPV is given by

$\begin{array}{cc}\mathrm{SOMPV}=\left(S_o-S_{or}\right)\times \varphi \times D_z\times NTG& \left(3\right)\end{array}$

with NTG being the net-to-gross ratio, S_o being the oil saturation and S_or being the residual oil saturation. These quantities can be computed for any block in the simulation grid.

FIG. 3A shows an example 2D top view of an ROI based 3D sweet spot map. As shown in FIG. 3A, grid cells with ROI exceeding a pre-determined threshold (e.g., 0.6) are included the sweet spot map. For example, grid cells (300a, 301a) are included. In other words, grid cells with ROI less than the pre-determined threshold (e.g., 0.6) are excluded from the sweet spot. In addition, grid cells intersected by or in the near region of existing wells are excluded to avoid interference between wells. The near region includes the drainage region of a corresponding well. The size of the near region is further defined by a user specified spacing input to the optimization process. The spacing input specifies the minimum distance allowable between two wells. Using the spacing input, a volume around each existing well is calculated from all directions and is excluded from the 3D sweet spot map. For example, the space input may be specified by reservoir engineers depending on the reservoir connectivity and on engineering practice of a given field.

The resultant 3D sweet spot map is clustered using a clustering algorithm (e.g., density-based spatial clustering of applications with noise (DBSCAN)) to generate distinct 3D clusters. Each cluster includes a collection of nearby grid cells in the 3D sweet spot map and corresponds to one volume in the reservoir that can be consumed by one well. In other words, each cluster defines the volume of the reservoir where contained hydrocarbons are allocated to be extracted by a single well. FIG. 3B shows an example 2D top view of clusters generated using DBSCAN algorithm from the sweet spot map depicted in FIG. 3A above. For example, clusters (300b, 301b) correspond to region 3 and region 4, respectively.

The second part of the well placement optimization process performed by the well design engine is generating trajectories of wells. For each cluster, a well is placed. In other words, one entire individual well is placed in each cluster. by using 3D Orthogonal Distance Regression to generate a best-fit line segment (referred to as a regression line segment) in the corresponding volume of the reservoir. Orthogonal Distance Regression (ODR) is a regression technique in which observational errors on both dependent and independent variables are considered. This best-fit line segment represents the trajectory path of the well which minimizes the proximity to the grid cells in the corresponding cluster. FIG. 3C shows an example 3D diagram of several regression line segments generated in different clusters. For example, the regression line segment (300c) is generated from the cluster (301c) corresponding to the cluster (300b) in the 2D top view depicted in FIG. 3B above. Similarly, the regression line segment (302c) is generated from the cluster (303c) corresponding to the cluster (301b) in the 2D top view depicted in FIG. 3B above. FIG. 3D shows an example 3D diagram of a new well (300d) placed according to the regression line segment (302c) depicted in FIG. 3C above. Similarly, FIG. 3E shows an example 3D diagram of a new well (300e) placed according to the regression line segment (300c) depicted in FIG. 3C above.

FIG. 2 shows a flowchart in accordance with one or more embodiments disclosed herein. One or more of the steps in FIG. 2 may be performed by the well design and placement analyzer (161) and other components of the field (100), discussed above in reference to FIG. 1. In one or more embodiments, one or more of the steps shown in FIG. 2 may be omitted, repeated, and/or performed in a different order than the order shown in FIG. 2. Accordingly, the scope of the disclosure should not be considered limited to the specific arrangement of steps shown in FIG. 2.

Initially in Block 200, a field development plan is obtained. The field development plan specifies a schedule of drilling new wells in the field from a start time until an end time spanning a multi-year duration. For example, one or more new wells may be specified to start drilling during one or more months of one or more calendar years for the multi-year duration of the field development plan. For each scheduled drilling of the new wells throughout the multi-year duration, a request for performing well placement optimization may be specified in the field development plan.

In Block 201, at each simulation time-step during a reservoir simulation of the field, the field development plan is analyzed regarding any scheduled drilling of a new well that is specified to perform well placement optimization. In one or more embodiments, the reservoir simulation is performed for a sequence of simulation time-steps spanning from the start time till the end time of the multi-year duration in the field development plan. In other words, the sequence of simulation time-steps corresponds to a sequence of time points in the multi-year duration.

In Block 202, a determination is made as to whether a scheduled drilling of a new well is specified to occur at the current reservoir simulation time-step, and whether the new well is to be placed by performing well placement optimization. If the determination is positive, i.e., a scheduled drilling of a new well is specified to perform well placement optimization at the current reservoir simulation time-step, the method proceeds to Block 203. If the determination is negative, i.e., no scheduled drilling of a new well is specified to perform well placement optimization at the current reservoir simulation time-step, the method proceeds to Block 212.

In Block 203, a three-dimensional (3D) sweet spot map is calculated or otherwise generated. For example, the 3D sweet spot map may be generated based on the ROI as the quality measure. In one or more embodiments, the sweet spot map is calculated many times throughout the numerical simulation runtime (i.e. in one simulation run, the sweet spot map may be generated multiple times). Business plans usually involve a set of new wells scheduled for drilling at different phases. A subset of the wells is placed at each phase. Therefore, during the numerical simulation run, the sweet spot map is calculated when the simulation reaches the date of the next phase. Then, wells are placed and the numerical simulation continuous. During the same simulation run, when it reaches the date of the next phase, the sweet spot map is calculated again. The wells are placed based on the newly calculated sweet spot map and the numerical simulation continuously. This is important because sweet spot maps are dynamic in nature and will change from one time-step to another.

In Block 204, the 3D sweet spot map is normalized. In one or more embodiments, the normalization is performed as follows:

$\begin{array}{cc}{y}_i=\frac{x_i-\min \left(x\right)}{\max \left(x\right)-\min \left(x\right)}& \left(4\right)\end{array}$

where y_i is the new normalized sweet spot index for each grid cell, x_i is the actual sweet spot value for the grid cell, min and max are minimum and maximum values of the sweet spots, respectively.

In Block 205, the grid cells that are intersected by existing wells are excluded from the 3D sweet spot map. Existing well locations are obtained from the reservoir model at the current reservoir simulation time-step and excluded from the 3D sweet spot map. The existing well locations at the current reservoir simulation time-step correspond to the number of existing wells that have been drilled at a corresponding time point in the multi-year duration according to the field development plan.

In Block 206, the 3D sweet spot map is passed to the well placement optimization process.

In Block 207, the well placement optimization process is performed based on the 3D sweet spot map.

In Block 208, the 3D sweet spot map is filtered and clustered. In one or more embodiments, the sweet spot map is filtered using percentile of normalized sweet spot values. The grid cells with high sweet spot values are selected. For example, if a percentile of 96% is specified as input to Block 208, the best 4% of the grid cells are selected. More specifically, a percentile of 96% (as an example) returns a cutoff value (e.g., 0.55) above which are the best grid cells (i.e., having values exceeding 0.55). All other grid cells are removed with the remaining grid cells used in clustering.

In one or more embodiments, DBSCAN is used as the clustering algorithm The clustering algorithm groups a number of grid cells together to form one volume to be targeted for placing wells. While alternative clustering algorithms such as K-MEANS requires prior knowledge of the number of clusters. DBSCAN, on the other hand, automatically finds the number of clusters without prior knowledge.

In Block 209, one or more well paths are created using regression at each cluster. For example, the well path may be created using Orthogonal Distance Regression (ODR) technique. Each well path created from a cluster corresponds to a new well. In one or more embodiments, a single regression line is created in each cluster. Each regression line created corresponds to a well path of one well.

In Block 210, trajectory points are obtained from the created well path of each cluster.

In Block 211, a new well is created and activated in the reservoir model based on the trajectory points created in the clusters.

In Block 212, the reservoir simulation including the new well is performed for the current simulation time-step. Accordingly, the new well is added to the existing well locations starting from the next reservoir simulation time-step consistent with the field development plan.

In Block 213, a determination is made as to whether the end of reservoir simulation has been reached. For example, the end of reservoir simulation may be the end time of the multi-year duration of the field development plan. If the determination is positive, i.e., the end of reservoir simulation has been reached, the method ends. If the determination is negative, i.e., the end of reservoir simulation has not been reached, the method returns to Block 201.

FIGS. 3F-3I show an example in accordance with one or more embodiments. The example shown in FIGS. 3F-3I is based on the system and method described in reference to FIGS. 1-2 above. In particular, FIGS. 3F-3I show an example of well design and placement according to a field development plan described in reference to FIGS. 1-2 above. In one or more embodiments, one or more of the modules and/or elements shown in FIGS. 3F-3I may be omitted, repeated, combined and/or substituted. Accordingly, embodiments disclosed herein should not be considered limited to the specific arrangements of modules and/or elements shown in FIGS. 3F-3I.

To illustrate the advantage of the well design and placement analyzer and method described above, the placement results of ten wells are compared between three scenarios using the time-varying sweet spot map with respect to using a single static sweet spot map. In all three scenarios, the sweet spot maps are generated using the ROI as the quality measure, and the well design and placement are performed using clustering and regression algorithms.

FIG. 3F illustrates the placement result of the first scenario that places 10 wells (e.g., well (310a) and well (310b)), independent of any reservoir simulation, in a sweet spot map (2020) that is generated based on existing wells at year 2020 as specified in the field development plan.

FIG. 3G illustrates the placement result of the second scenario that places 10 wells, independent of any reservoir simulation, in a sweet spot map (2028) that is generated based on existing wells at year 2028 as specified in the field development plan. While the well (310a) is placed at the same location in both first and second scenarios, the well (310b) and well (310c) are unique in each scenario. In both the first and second scenarios, the 10 placed wells are scheduled to be drilled over the multi-year duration from year 2020 through year 2028.

FIG. 3H illustrates the third scenario that places 10 wells, concurrent with the reservoir simulation, based on multiple evaluations of sweet spot maps and multiple well placement optimization runs. As shown in FIG. 3H, the placement results (320a), (320b), (320c), (320d), and (320e) are based on the sweet spots (2020), (2022), (2024), (2026), and (2028), respectively. The sweet spots (2020), (2022), (2024), (2026), and (2028) are generated based on existing well locations at years 2020, 2022, 2024, 2026, and 2028, respectively, as specified in the field development plan. Further as shown in FIG. 3H, the field development plan specifies total 2 drilled wells by year 2020, total 4 drilled wells by year 2022, total 6 drilled wells by year 2024, total 8 drilled wells by year 2026, and total 10 drilled wells by year 2028.

FIG. 3I shows the reservoir simulation results of the three scenarios. As shown in FIG. 3I, the simulated production rates (320a, 321a, 322a, 323a) correspond to the first scenario where the sweet spot map generated for year 2020 is used, the simulated production rates (320b, 321b, 322b, 323b) correspond to the second scenario where the sweet spot map generated for year 2028 is used, and the simulated production rates (320c, 321c, 322c, 323c) correspond to the third scenario where the time-varying sweet spot maps generated concurrently with the reservoir simulation are used. The reservoir simulation results of the third scenario exhibit an extended plateau (i.e., longer with consistently high production performance) from year 2020 through year 2031 when compared to the first and second scenarios. This consistent production performance of the third scenario is because each well is placed optimally at the right time as the reservoir simulation progresses through the simulation time-steps. From year 2020 through year 2028 when new wells are drilled each year and added to the production, controls are applied to the wells to limit production to certain rates, as reflected in the numerical simulation. The newly added wells have the potential to produce more but are controlled and limited to produce at certain rates. Therefore, instead of increasing the field oil production rate from year 2020 through year 2028, the newly added wells contribute to extending the plateau.

Embodiments may be implemented on a computer system. FIG. 4 is a block diagram of a computer system (400) used to provide computational functionalities associated with described algorithms, methods, functions, processes, flows, and procedures as described in the instant disclosure, according to an implementation. The illustrated computer (402) is intended to encompass any computing device such as a high-performance computing (HPC) device, a server, desktop computer, laptop/notebook computer, wireless data port, smart phone, personal data assistant (PDA), tablet computing device, one or more processors within these devices, or any other suitable processing device, including both physical or virtual instances (or both) of the computing device. Additionally, the computer (402) may include a computer that includes an input device, such as a keypad, keyboard, touch screen, or other device that can accept user information, and an output device that conveys information associated with the operation of the computer (402), including digital data, visual, or audio information (or a combination of information), or a GUI.

The computer (402) can serve in a role as a client, network component, a server, a database or other persistency, or any other component (or a combination of roles) of a computer system for performing the subject matter described in the instant disclosure. The illustrated computer (402) is communicably coupled with a network (430). In some implementations, one or more components of the computer (402) may be configured to operate within environments, including cloud-computing-based, local, global, or other environment (or a combination of environments).

At a high level, the computer (402) is an electronic computing device operable to receive, transmit, process, store, or manage data and information associated with the described subject matter. According to some implementations, the computer (402) may also include or be communicably coupled with an application server, e-mail server, web server, caching server, streaming data server, business intelligence (BI) server, or other server (or a combination of servers).

The computer (402) can receive requests over network (430) from a client application (for example, executing on another computer (402)) and responding to the received requests by processing the said requests in an appropriate software application. In addition, requests may also be sent to the computer (402) from internal users (for example, from a command console or by other appropriate access method), external or third-parties, other automated applications, as well as any other appropriate entities, individuals, systems, or computers.

Each of the components of the computer (402) can communicate using a system bus (403). In some implementations, any or all of the components of the computer (402), both hardware or software (or a combination of hardware and software), may interface with each other or the interface (404) (or a combination of both) over the system bus (403) using an application programming interface (API) (412) or a service layer (413) (or a combination of the API (412) and service layer (413). The API (412) may include specifications for routines, data structures, and object classes. The API (412) may be either computer-language independent or dependent and refer to a complete interface, a single function, or even a set of APIs. The service layer (413) provides software services to the computer (402) or other components (whether or not illustrated) that are communicably coupled to the computer (402). The functionality of the computer (402) may be accessible for all service consumers using this service layer. Software services, such as those provided by the service layer (413), provide reusable, defined business functionalities through a defined interface. For example, the interface may be software written in JAVA, C++, or other suitable language providing data in extensible markup language (XML) format or other suitable format. While illustrated as an integrated component of the computer (402), alternative implementations may illustrate the API (412) or the service layer (413) as stand-alone components in relation to other components of the computer (402) or other components (whether or not illustrated) that are communicably coupled to the computer (402). Moreover, any or all parts of the API (412) or the service layer (413) may be implemented as child or sub-modules of another software module, enterprise application, or hardware module without departing from the scope of this disclosure.

The computer (402) includes an interface (404). Although illustrated as a single interface (404) in FIG. 4, two or more interfaces (404) may be used according to particular needs, desires, or particular implementations of the computer (402). The interface (404) is used by the computer (402) for communicating with other systems in a distributed environment that are connected to the network (430). Generally, the interface (404) includes logic encoded in software or hardware (or a combination of software and hardware) and operable to communicate with the network (430). More specifically, the interface (404) may include software supporting one or more communication protocols associated with communications such that the network (430) or interface's hardware is operable to communicate physical signals within and outside of the illustrated computer (402).

The computer (402) includes at least one computer processor (405). Although illustrated as a single computer processor (405) in FIG. 4, two or more processors may be used according to particular needs, desires, or particular implementations of the computer (402). Generally, the computer processor (405) executes instructions and manipulates data to perform the operations of the computer (402) and any algorithms, methods, functions, processes, flows, and procedures as described in the instant disclosure.

The computer (402) also includes a memory (406) that holds data for the computer (402) or other components (or a combination of both) that can be connected to the network (430). For example, memory (406) can be a database storing data consistent with this disclosure. Although illustrated as a single memory (406) in FIG. 4, two or more memories may be used according to particular needs, desires, or particular implementations of the computer (402) and the described functionality. While memory (406) is illustrated as an integral component of the computer (402), in alternative implementations, memory (406) can be external to the computer (402).

The application (407) is an algorithmic software engine providing functionality according to particular needs, desires, or particular implementations of the computer (402), particularly with respect to functionality described in this disclosure. For example, application (407) can serve as one or more components, modules, applications, etc. Further, although illustrated as a single application (407), the application (407) may be implemented as multiple applications (407) on the computer (402). In addition, although illustrated as integral to the computer (402), in alternative implementations, the application (407) can be external to the computer (402).

There may be any number of computers (402) associated with, or external to, a computer system containing computer (402), each computer (402) communicating over network (430). Further, the term “client,” “user,” and other appropriate terminology may be used interchangeably as appropriate without departing from the scope of this disclosure. Moreover, this disclosure contemplates that many users may use one computer (402), or that one user may use multiple computers (402).

In some embodiments, the computer (402) is implemented as part of a cloud computing system. For example, a cloud computing system may include one or more remote servers along with various other cloud components, such as cloud storage units and edge servers. In particular, a cloud computing system may perform one or more computing operations without direct active management by a user device or local computer system. As such, a cloud computing system may have different functions distributed over multiple locations from a central server, which may be performed using one or more Internet connections. More specifically, cloud computing system may operate according to one or more service models, such as infrastructure as a service (IaaS), platform as a service (PaaS), software as a service (SaaS), mobile “backend” as a service (MBaaS), serverless computing, artificial intelligence (AI) as a service (AlaaS), and/or function as a service (FaaS).

Although only a few example embodiments have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the example embodiments without materially departing from this invention. Accordingly, all such modifications are intended to be included within the scope of this disclosure as defined in the following claims.

Claims

1. A method to perform drilling operations in a field, comprising:

obtaining a field development plan of the field, the field development plan comprising a new well drilling schedule for a multi-year duration;

performing, through a sequence of simulation time-steps corresponding to a sequence of time points in the multi-year duration, a reservoir simulation based on a reservoir model of the field;

determining, during a first simulation time-step of the reservoir simulation, that the new well drilling schedule comprises drilling a first new well at a first time point corresponding to the first simulation time-step;

generating, in response to said determining, a first sweet spot map for drilling the first new well, the first sweet spot map excludes a first plurality of existing well locations in the reservoir model at the first simulation time-step;

generating, based on the first sweet spot map, a first well trajectory of the first new well for adding to the reservoir model;

generating, based at least on the first well trajectory of the first new well added to the reservoir model, a first simulation result of the reservoir simulation; and

drilling, based at least on the first simulation result and at the first time point of the multi-year duration, the first new well according to the first well trajectory in the field.

2. The method of claim 1,

wherein generating the first sweet spot map is further in response to a request specified in the new well drilling schedule to optimize well placement of the first new well.

3. The method of claim 1,

wherein generating the first sweet spot map for drilling the first new well is based on a reservoir opportunity index.

4. The method of claim 1, wherein generating the first well trajectory of the first new well comprises:

generating, using a pre-determined clustering algorithm, a plurality of clusters of the first sweet spot map; and

generating a plurality of well paths from the plurality of clusters,

wherein the first well trajectory is generated based on the plurality of well paths.

5. The method of claim 4, wherein generating the plurality of well paths comprises:

applying a pre-determined regression algorithm to each of the plurality of clusters to generate a regression line segment; and

generating a plurality of well paths from the plurality of clusters,

wherein the first well trajectory is generated based on the plurality of well paths.

6. The method of claim 5,

wherein the pre-determined regression algorithm comprises Orthogonal Distance Regression (ODR) algorithm.

7. The method of claim 1, further comprising:

further determining, during a second simulation time-step of the reservoir simulation subsequent to the first simulation time-step, that the new well drilling schedule comprises drilling a second new well at a second time point corresponding to the second simulation time-step;

generating, in response to said further determining, a second sweet spot map for drilling the second new well, the second sweet spot map excludes a second plurality of existing well locations in the reservoir model at the second simulation time-step, the second plurality of existing well locations comprise the first well trajectory of the first new well;

generating, based on the second sweet spot map, a second well trajectory of the second new well for adding to the reservoir model;

generating, based at least on the second well trajectory of the second new well added to the reservoir model, a second simulation result of the reservoir simulation; and

drilling, based at least on the second simulation result and at the second time point of the multi-year duration, the second new well according to the second well trajectory in the field.

8. A well design and placement analyzer to facilitate drilling operations in a field, comprising:

a computer processor; and

memory storing instructions, when executed by the computer processor comprising functionality for: obtaining a field development plan of the field, the field development plan comprising a new well drilling schedule for a multi-year duration, wherein a reservoir simulation is performed through a sequence of simulation time-steps corresponding to a sequence of time points in the multi-year duration based on a reservoir model of the field, and wherein the new well drilling schedule comprises drilling a first new well at a first time point corresponding to a first simulation time-step of the sequence of simulation time-steps; generating, during the first simulation time-step, a first sweet spot map for drilling the first new well, the first sweet spot map excludes a first plurality of existing well locations in the reservoir model at the first simulation time-step; and generating, based on the first sweet spot map, a first well trajectory of the first new well for adding to the reservoir model, wherein a first simulation result of the reservoir simulation is generated based at least on the first well trajectory of the first new well added to the reservoir model; and wherein the first new well is drilled, based at least on the first simulation result and at the first time point of the multi-year duration, according to the first well trajectory in the field.

9. The well design and placement analyzer of claim 8,

wherein generating the first sweet spot map is further in response to a request specified in the new well drilling schedule to optimize well placement of the first new well.

10. The well design and placement analyzer of claim 8,

wherein generating the first sweet spot map for drilling the first new well is based on a reservoir opportunity index.

11. The well design and placement analyzer of claim 8, wherein generating the first well trajectory of the first new well comprises:

generating, using a pre-determined clustering algorithm, a plurality of clusters of the first sweet spot map; and

generating a plurality of well paths from the plurality of clusters,

wherein the first well trajectory is generated based on the plurality of well paths.

12. The well design and placement analyzer of claim 11, wherein generating the plurality of well paths comprises:

applying a pre-determined regression algorithm to each of the plurality of clusters to generate a regression line segment; and

generating a plurality of well paths from the plurality of clusters,

wherein the first well trajectory is generated based on the plurality of well paths.

13. The well design and placement analyzer of claim 12,

wherein the pre-determined regression algorithm comprises Orthogonal Distance Regression (ODR) algorithm.

14. A system comprising:

a wellsite for performing drilling operations in a field;

a reservoir simulator comprising the functionality for: obtaining a field development plan of the field, the field development plan comprising a new well drilling schedule for a multi-year duration; performing, through a sequence of simulation time-steps corresponding to a sequence of time points in the multi-year duration, a reservoir simulation based on a reservoir model of the field; and determining, during a first simulation time-step of the reservoir simulation, that the new well drilling schedule comprises drilling a first new well at a first time point corresponding to the first simulation time-step; and

a well design and placement analyzer comprising the functionality for: generating, in response to said determining, a first sweet spot map for drilling the first new well, the first sweet spot map excludes a first plurality of existing well locations in the reservoir model at the first simulation time-step; and generating, based on the first sweet spot map, a first well trajectory of the first new well for adding to the reservoir model,

wherein the reservoir simulator further comprising the functionality for generating, based at least on the first well trajectory of the first new well added to the reservoir model, a first simulation result of the reservoir simulation, and

wherein the first new well is drilled, based at least on the first simulation result and at the first time point of the multi-year duration, at the wellsite according to the first well trajectory.

15. The system of claim 14,

wherein generating the first sweet spot map is further in response to a request specified in the new well drilling schedule to optimize well placement of the first new well.

16. The system of claim 14,

wherein generating the first sweet spot map for drilling the first new well is based on a reservoir opportunity index.

17. The system of claim 14, wherein generating the first well trajectory of the first new well comprises:

generating, using a pre-determined clustering algorithm, a plurality of clusters of the first sweet spot map; and

generating a plurality of well paths from the plurality of clusters,

wherein the first well trajectory is generated based on the plurality of well paths.

18. The system of claim 17, wherein generating the plurality of well paths comprises:

applying a pre-determined regression algorithm to each of the plurality of clusters to generate a regression line segment; and

generating a plurality of well paths from the plurality of clusters,

wherein the first well trajectory is generated based on the plurality of well paths.

19. The system of claim 18,

wherein the pre-determined regression algorithm comprises Orthogonal Distance Regression (ODR) algorithm.

20. The system of claim 14,

wherein the reservoir simulator further comprises the functionality for further determining, during a second simulation time-step of the reservoir simulation subsequent to the first simulation time-step, that the new well drilling schedule comprises drilling a second new well at a second time point corresponding to the second simulation time-step, and

wherein the well design and placement analyzer further comprises the functionality for: generating, in response to said further determining, a second sweet spot map for drilling the second new well, the second sweet spot map excludes a second plurality of existing well locations in the reservoir model at the second simulation time-step, the second plurality of existing well locations comprise the first well trajectory of the first new well; and generating, based on the second sweet spot map, a second well trajectory of the second new well for adding to the reservoir model,

wherein a second simulation result of the reservoir simulation is generated based at least on the second well trajectory of the second new well added to the reservoir model, and

wherein the first new well is drilled, based at least on the second simulation result and at the second time point of the multi-year duration, according to the second well trajectory in the field.