METHOD TO DETERMINE A LOCATION FOR PLACING A WELL WITHIN A TARGET RESERVOIR

Abstract

A method of drilling a wellbore. The method may include collecting data for a plurality of well stages in at least one data reservoir. The method may also include creating a priority group of well stages from the plurality of well stages in the at least one data reservoir. The method may also include validating the well stages included within the priority group to create a validated priority group. The method may also include analyzing the data for the validated priority group to determine an effective conductivity value for each well stage in the validated priority group. The method may also include using the effective conductivity value to determine a target location for drilling the wellbore in the target reservoir. The method may also include drilling a well at the target location in the target reservoir.

Description

RELATED APPLICATION

This application claims the benefit, under 35 USC § 119(e), of the filing of U.S. Provisional Patent Application Ser. No. 62/529,914 filed on Jul. 7, 2017 and entitled “Method to Determine and Predict an Effective Conductivity in a Target Reservoir” to Christopher Louis Beato, which is incorporated herein by reference in its entirety.

FIELD

The present embodiments generally relate to methods for drilling a well within a target reservoir, particularly, to methods for determining a location for placing a well within a target reservoir, for example, by predicting the effective conductivity by assessing quantity of natural fracture systems and paths of weakness within the target reservoir.

BACKGROUND

Well testing has been used for decades to determine essential reservoir properties and to assess wellbore conditions prior to, during, and after a fracture stimulation of a well. There are many different types of tests that can be utilized to collect various pieces of information. The tests employed can vary based upon various factors, such as well location, well type, formation type, and the like.

One specific measure of a reservoir is a conductivity value (kh). The conductivity value is a representation of the cumulative effect of matrix permeability, natural fracture systems and paths of weakness extant in a reservoir. In a shale or mudstone reservoir the matrix permeability may be very low so the conductivity value represents the natural fracture systems and paths of weakness extant in the formation.

It may be desirable to drill into target reservoirs with high conductivity values, as this means that the target reservoir has a higher number of natural fracture systems and paths of weakness, which will result in a more productive well and a greater return on investment.

While a great deal of data is often collected in the oil and gas industry, methods of predicting conductivity values based upon the available data have heretofore not been practical or accurate,

A need exists, therefore, to leverage existing, available data to improve the placement of wells within a reservoir, for example, by accurately qualitatively predicting the level or quantity of natural fracture systems and paths of weakness prior to drilling.

Thus, a need exists for determining optimum locations for placing a well within a target reservoir, such as by determining the effective conductivity by assessing quantity of natural fracture systems and paths of weakness within the target reservoir. Being able to assess a qualitatively “better” location to drill into a target reservoir can result in a great deal of cost savings, increased well productivity, higher return on investment, improved safety of personnel, and lessened impact on the environment due to fewer needed wells, and other benefits.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description will be better understood in conjunction with the accompanying drawings as follows:

FIG. 1 depicts a flow diagram of the method according to one or more embodiments.

FIG. 2 is a three dimensional representation of a data reservoir.

FIG. 3 is a diagram illustrating a relationship between pre-fracture and post-fracture data.

FIG. 4 is a three dimensional representation of a data reservoir.

FIG. 5 is a three dimensional representation of a data reservoir.

FIG. 6 is a three dimensional representation of a data reservoir.

FIG. 7 is a three dimensional representation of a data reservoir.

FIG. 8 is a three dimensional representation of a data reservoir.

The present embodiments are detailed below with reference to the listed Figures.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Before explaining the present method in detail, it is to be understood that the method is not limited to the particular embodiments and that it can be practiced or carried out in various ways.

Specific structural and/or functional details disclosed herein are not to be interpreted as limiting, but merely as a basis of the claims and as a representative basis for teaching persons of ordinary skill in the art, upon viewing this disclosure, to variously employ the present embodiments.

Disclosed herein are methods generally related to drilling a well within a target reservoir, more particularly, by determining a location for placing a well within a target reservoir. In some embodiments, the methods may qualitatively predict an effective conductivity of the target reservoir. Additionally or alternatively, in some embodiments the methods may quantify relative predictions to further distinguish predicted effective conductivity values. As will be disclosed herein, the predicted effective conductivity values may be used to determine one or more optimum locations within a target reservoir.

In some embodiments, the methods may be suitable for application to reservoirs termed by persons of ordinary skill in the art as “unconventional reservoirs.” An “unconventional reservoir,” as used herein, may refer to a reservoir containing oil and gas that requires special recovery operations. Examples if unconventional reservoirs can include, but are not limited to, gas and oil shales, coalbed methane, tight-gas sands, gas-hydrate deposits, fractured reservoirs, reservoirs with low matrix permeability, heavy oil and tar sands, and similar reservoirs known in the industry.

In some embodiments, the methods may predict the character and/or quantity of natural fracture systems and other paths of weakness within a target reservoir, on the basis of data from a data reservoir. “Natural fracture systems or paths of weakness,” as used herein, may refer to fluid flow pathways within an unconventional reservoir which can allow oil and gas to move through the formation, for example, so as to be produced, “Target reservoir,” as used herein, may refer to a three dimensional volume of rock containing oil or gas that is to be developed. “Data reservoir,” as used herein, may refer to a three dimensional volume of rock containing oil or gas for which there is data, such as data resulting from a fracturing or other stimulation operation or data from a diagnostic test or operation. In embodiments, the data reservoir and the target reservoir may be the same reservoir.

Referring to FIG. 1, an embodiment of a method 100 for determining a location for placing a well within a target reservoir is illustrated as a flow diagram.

Organizing Data from the Data Reservoir

In some embodiments, the methods may generally include organizing data from the data reservoir. The data reservoir can be any reservoir for which the desired fracture stimulation data and/or diagnostic data is present. In embodiments, the data reservoir can also be the target reservoir. For example, it may be desirable to select an optimum drilling spot within a reservoir which has been produced, previously, for a period of time. In alternate embodiments, the target reservoir can be a completely separate reservoir which is believed to have similar characteristics, in one or more aspects.

In some embodiments, the data may include fracture stimulation data. In various embodiments, fracture stimulation data can refer to a wide variety of characteristics, attributes, or parameters associated with the performance of a fracture stimulation. “Fracture stimulation,” as used herein, may refer to the process of pumping a fluid into a well at high pressure in order to propagate a hydraulic fracture away from a wellbore and into a target reservoir allowing a reservoir to be stimulated, thereby creating and enhancing fluid flow pathways and allowing oil and gas to be produced. Examples of fracture stimulation data may include, but are not limited to, reservoir pressure, breakdown pressure, initial shut in pressure, fracture closure pressure, fracture breakdown pressure, conductivity, pump pressure, pump rate, clean fluid volume (fluid without proppant), fracturing fluid volume, pounds of proppant pumped, proppant concentration, temperature of the fluid being pumped, the duration of the fracturing operation and/or the time, the specific gravity of the fluid being pumped, the density of the fluid being pumped, downhole pressure, downhole rate of fluid, and the like.

For example, in the embodiment of FIG. 1, the method 100 includes collecting fracture stimulation data for a plurality of well stages in at least one data reservoir, as illustrated in step 102, “Well stage,” as used herein, may refer to a specific hydraulic fracture operation at any location in a wellbore. For example, FIG. 2 is a three dimensional representation of a data reservoir 200 having a plurality of well stages 210 disposed with the reservoir 200.

Additionally or alternatively, in some embodiments, the data may include data associated with or resulting from one or more diagnostic tests. Diagnostic tests associated with wells and/or well stages are generally known to persons of ordinary skill in the art. An example of such diagnostic tests includes a Diagnostic Formation Injection Test (DFIT), which is a relatively long-duration, relatively low-volume fracturing operation in which a volume of fracturing fluid (such as potassium chloride or a similar fluid) is pumped until fracture-initiation and, thereafter, the pressure within the well may be monitored for a period of time, for example, until fracture closure (e.g., for a period of several hours or even a few days). Another example of such diagnostic tests includes a Fluid Efficiency Test (FET), which is a relatively short-duration, relatively low-volume fracturing operation in which a volume of fluid is injected into a well stage to observe break-down followed by a shut-in period (e.g., for a period of several minutes or hours), for example, so as to observe instantaneous shut-in pressure (ISIP) subsequent to leak-off of the pressure. “Instantaneous shut-in pressure (ISIP),” as used herein, may refer to the recorded pump pressure immediately after pumps are shut down, for example, during a fracturing operation or diagnostic test. “Pressure leak-off,” as used herein, can refer to the changes in pressure as recorded at the well from the time that pumps are shut off, for example, over the course of a predetermined time interval.

In the embodiment of FIG. 1, the method 100 includes collecting diagnostic test data for the plurality of well stages in the at least one data reservoir, as illustrated in step 104, While in the embodiment of FIG. 1 the method 100 includes both collecting fracture stimulation data for a plurality of well stages in at least one data reservoir, as illustrated in step 102, and collecting diagnostic test data for the plurality of well stages in the at least one data reservoir, as illustrated in step 104, in some other embodiments, a method may utilize only fracture stimulation data or only data from a diagnostic test. For example, in some embodiments the data may be collected from any data source or combination of data sources from which a pressure leak-off rate can be established over a predetermined duration with respect to one or more well stages. Additionally or alternatively, in some embodiments the data collected may be from any data source or combination of data sources from which a conductivity can be established with respect to one or more well stages. For example, invarious embodiments, pressure leak-off data may be determined based upon measurements via a wellhead sensor or a downhole sensor. As additional examples, conductivity may be measured, determined, estimated, or extrapolated based upon injection testing, side-wall cores, mud gas logs, and the like. For example, in some embodiments, the data may be associated with pressure pulses resulting from an occurrence of a water hammer effect within a wellbore, “Water hammer effect,” as used herein, may describe a sudden shut down of pumps during a fracture stimulation which results in the formation of a series of pressure pulses, known as a water hammer effect, due to the confined space of the wellbore, the reservoir conductivity, and volume. For example, the water hammer effect within a wellbore may be used to approximate reservoir conductivity resulting from natural fractures and/or other paths of weakness within the surrounding formation.

In some embodiments, organizing the data from the data reservoir may further include creating a priority group of well stages from the plurality of well stages in the at least one data reservoir. “Priority group of well stages,” as used herein, may refer to wells or well stages generally believed by a person having ordinary skill in the art as being similar to each other and to a target reservoir in at least one aspect. For example, in the embodiment of FIG. 1, the method 100 includes creating a priority group of well stages from the plurality of well stages in the at least one data reservoir, as illustrated in step 106.

Persons of ordinary skill in the art, upon viewing this disclosure, will appreciate those aspects in which well stages may be similar to each other and/or to the target reservoir. For example, in some embodiments a well stage may be selected for inclusion within the priority group on the basis of having a similar formation pressure, on the basis of formation similarities, on the basis of similar gamma ray measurements, on the basis of similar resistivity measurements, on the basis of similar fracture stimulation fluids used, on the basis of similar amounts of proppant used, on the basis of similar diagnostic tests used, and combinations thereof. In some, more particular embodiments, a person having ordinary skill in the art may select a data reservoir on the basis of having similarities to the target such as rock matrix characteristics (e.g., the same or substantially similar depositional environments) and/or the same or substantially similar basis reservoir pressure. For example, a skilled artisan may want to include well stages having the same or substantially similar reservoir pressures in a priority group in that similarities in reservoir pressure may serve as an indication of reservoir depletion (e.g., a well stage within a pressure-depleted reservoir may exhibit a completely different leakoff rate due to the lower pressure in comparison to a relatively undepleted reservoir).

Also, persons of ordinary skill in the art, upon viewing this disclosure, will appreciate to the degree to which a given aspect may be similar between two well stages and/or between a well stage and to the target reservoir. For example, a well stage that varies from other well stages included within the priority group and/or from the target reservoir by more than about one standard deviation in a given aspect, such as instantaneous shut in pressure, may be determined as dissimilar, and therefore, not included within the priority group. However, the amount of acceptable variation can be selected based upon the application and the particular aspect.

The priority group may represent an initial set of well stages. The priority group can be selected by persons of ordinary skill in the art, upon viewing this disclosure, having knowledge of a plurality of well stages. Additionally or alternatively, the priority group can be selected by mining existing data, for example, from a database including a plurality of well stages, for specific parameters or aspects. In some embodiments, the well stages within the priority group may be wells within the target reservoir that have not, or likely have not, experienced pressure interference from a neighboring well.

In some embodiments, organizing the data from the data reservoir may further include validating the well stages included within the priority group to create a validated priority group. Generally, during validation, outlier well stages may be eliminated from the priority group, for example, due to dissimilarities between a well stage and the other priority group members, differences in fracturing techniques between a well stage and the other priority group members (e.g. differences in fracturing fluid composition), abnormalities associated with a particular well stage, such mechanical failures, screen outs, and the like. “Mechanical problems,” as used herein, may refer to any of a variety of mechanical equipment malfunctions, such as gauges that may not be reading correctly, or any other equipment failure relative to a well at any time during drilling, completion, or other operations. “Screen out,” as used herein, may refer to an unplanned event that occurs when a hydraulic fracture stimulation has terminated because an injection pressure has reached its upper limit. For example, when the well injection pressure nears the maximum allowable working pressure of the fracturing equipment in use or of the well itself.

For example, in the embodiment of FIG. 1, the method 100 includes validating the well stages included within the priority group to create a validated priority group, as illustrated in step 108. For example, in various embodiments, validating the well stages may include identifying each well stage in the plurality of well stages in the at least one data reservoir which has experienced a screen out or has experienced mechanical problems, identifying a stimulation design for each well stage in the plurality of well stages in the at least one data reservoir, or the like. Persons of ordinary skill in the art, upon viewing this disclosure, will be able to selectively eliminate outlying well stages so as to validate the well stages selected for the priority group, for example, by reviewing information associated with a given well stage. Additionally or alternatively, the priority group can be validated by mining the data associated with the well stages within the priority group, for example, from a database including a plurality of well stage for outlying parameters or aspects or substantial deviations in parameters or aspects.

In some embodiments, outlier well stages can be grouped and analyzed separately to provide valuable information. In embodiments, persons of ordinary skill in the art, upon viewing this disclosure, can create a priority group of outliers. For example, in some embodiments one or more priority groups of outlying well stages (e.g., well stages disqualified from the priority group on the basis of having screened out, having exhibitd too high of a fracture propagation pressure, or the like) may form a priority group with may be analyzed to provide data in addition to the data from the validated priority group. For example, one or more priority groups of outlying well stages may be effective to provide negative data, such as excluding reservoirs and/or portions of a reservoir that are not desirable. In some embodiments, multiple priority groups can be created and analyzed for a gicen target reservoir.

Analyzing the Data from the Well Stages of the Validated Priority Group

In some embodiments, the method may generally include analyzing the data for each of the well stages of the validated priority group. Generally, in some embodiments, the data associated with each of the well stages can be analyzed to determine a pressure leak-off rate for each of the well stage, for example, in order to enable a determination of an effective post-fracture conductivity value for each well stage in the validated priority group. “Effective conductivity value,” as used herein, can refer to a measure of the conductivity of an unconventional reservoir due to the number of natural fractures or planes of weakness present.

In some embodiments, analyzing the data for each of the well stages of the validated priority group includes determining a pressure leak-off rate for each of the well stages of the validated priority group. The pressure leak-off rate may be calculated in any suitable manner as determined by persons of ordinary skill in the art, upon viewing this disclosure.

In some embodiments, the pressure leak-off rate for a given well stage may be determined using a suitable mathematical function. For example, in the embodiment of FIG. 1, the method 100 includes determining the pressure leak-off rate for a given well stage, as illustrated as step 110. An example of a mathematical function by which the pressure leak-off rate may be determined includes the G-function. Generally, the G-function is a dimensionless time function suitable for developing a linear relationship with respect to pressure behavior, such as during pressure leak-off. More particularly, the G function is an example of a dimensionless scaling factor. An example of the use of the G-function in determining pressure leak-off rate is provided in the examples, below. Persons of ordinary skill in the art, upon viewing this disclosure, will appreciate that other methods of mathematical manipulation can readily be applied in order to determine the pressure leak-off rate for each of the well stages.

In some embodiments, analyzing the data for each of the well stages of the validated priority group includes determining an effective conductivity value for each of the well stages of the validated priority group based upon the pressure leak-off rate of each of the respective well stages. For example, in the embodiment of FIG. 1, the method 100 includes determining an effective conductivity value for each of the well stages of the validated priority group, as illustrated at step 112.

In some embodiments, the effective conductivity value for one or more of the well stages may be expressed as a qualitative designation of the effective conductivity. In some embodiments, the pressure leak-off rate of a given well stage can be compared to pressure leak-off rates of the other well stages within the validated priority group, for example, to give qualitative values of conductivity. For example, well stages having a relatively high, rapid pressure leak-off rate may be characterized as having a high conductivity. Well stages having an intermediate, more gradual pressure leak-off rate may be characterized as having a medium or intermediate conductivity. Well stages having a relatively low, slow pressure leak-off rate may be characterized as having a low conductivity. In various embodiments, the well stages may be delineated, for example, between high-conductivity well stages, intermediate conductivity well stages, and low-conductivity well stages, based upon any suitable standard. For example, in some embodiments, well stages having a pressure leak-off rate that is within about one (1) standard deviation may be characterized as being an intermediate-conductivity well stage; well stages having a pressure leak-off rate of less than about one (1) standard deviation may be characterized as being a low-conductivity well stage; well stages having a pressure leak-off rate that is more than about one (1) standard deviation may be characterized as being a high-conductivity well stage.

Additionally or alternatively, in some embodiments the effective conductivity value for one or more of the well stages may comprise a quantitative value. In some embodiments a correlation (e.g., a mathematical relationship) may be developed between the pressure leak-off rate of a given well stage and the effective conductivity of that well stage. In various embodiments, the correlation between pressure leak-off rate and effective conductivity may be developed by any suitable methodology. For example, in some embodiments, the pressure leak-off rate may be related to (e.g., plotted with respect to) the pre-facture effective conductivity for those well stages within the validated priority group having both pressure leak-off data and effective conductivity data. In some embodiments, a best-fit equation may be developed based upon the data points relating (e.g., as a plot) pressure leak-off rate and effective conductivity. For example, in some embodiments, the best-fit equation correlating pressure leak-off rate and effective conductivity may be a polynomial equation. The relationship between pressure leak-off rate and effective conductivity can be used to determine, quantitatively, the effective post-fracture conductivity for each of the well stages of the validated priority group.

In some embodiments, determining the effective conductivity quantitatively, as opposed to qualitatively, may allow for greater resolution of the data associated with the well stages. For example, rather than designating the well stages as having high, intermediate, or conductivity, a quantitative determination of effective conductivity based upon leak-off rate can be made for the well stages within a validated priority group.

In some embodiments, analyzing the data for each of the well stage of the validated priority group includes verifying the effective conductivity that has been determined for each well stage of the validated priority group. In various embodiments, the effective conductivity that has been determined for each well stage can be compared to other data for each of the respective well stages and/or the associated well. For example, the effective conductivity that has been determined for each well stage can be compared to production logs, tracer surveys, seismic data, electric line logs, drill mud log data, or combinations thereof. The comparison between effective conductivity and this data (e.g., well data) may help to verify that the effective conductivity that was determined for each well stage.

In some embodiments, analyzing the data for each of the well stage of the validated priority group includes analyzing both pre-fracture and post-fracture data. For example, the ISIP for the pre-fracture diagnostic tests may be were plotted and compared to the post-fracture ISIP data for each well stage of the validataed priority group. A comparison plotted as a straight line may be effective to confirm that the same polynomial equation describing the best fit curve solving for effective conductivity (e.g., which may be developed with pre-fracture data) can be used with the rate of change calculated from the dimensionless data analysis of the post-fracture data. An example of such a correlation is illustrated in FIG. 3.

In some embodiments, analyzing the data for each of the well stages of the validated priority group includes creating a model of the data reservoir, for example, a three-dimensional (3D) model of the data reservoir. The effective conductivity data for each of the well stages, as determined herein, may be located within the 3D model. For example, FIGS. 4 and 5 are a two-dimensional (in the horizontal plane) and a three-dimensional representation of the data reservoir illustrated in FIG. 2. The examples of FIGS. 4 and 5 illustrate the variation in natural fracture density within the data reservoir.

Additionally, in some embodiments, the 3D model may include a variety of data associated with each of the well stages of the validated priority group. For example, in various embodiments, the 3D model may include production logs, tracer surveys, seismic data, electric line logs, drill mud log data, total organic content measurements from drill cuttings analyses, and/or whole core analyses for one or more of the well stages of the validated priority group. Seismic data may have many attributes that can be correlated to effective conductivity.

In some embodiments, the 3D model may be used to conduct multi-attribute and/or multivariate analysis of the data reservoir (as represented by the 3D model). Generally, the multi-attribute and/or multivariate analysis may apply machine learning processes to various data with regard to the data reservoir (e.g., as represented by the 3D model) to analyze multiple attributes simultaneously and, thereby, extract additional information from the 3D model. For example, FIGS. 6 and 7 are a two-dimensional (in the horizontal plane) and a three-dimensional representation of the data reservoir illustrated in FIGS. 2, 4, and 5. The examples of FIGS. 6 and 7 illustrate the results of a multi-attribute and/or multivariate analysis and shows the correlation between natural fracture density within the data reservoir and additional features of the reservoir, such as resistivity and porosity.

Examples of multi-attribute and/or multivariate analysis methods may make use of machine-learning techniques such as neural networks, a linear regression, a non-linear regression, or any other suitable means known to persons of ordinary skill in the art, upon viewing this disclosure. In some embodiments, commercially-available software may be employed to conduct at least a portion of the multi-attribute and/or multivariate analysis. Examples of suitable, commercially-available software packages include GGX; Paradise, commercially-available from Geophysical Insights; RSA; Landmark; and Transform, commercially-available from Drillers Info.

In some embodiments, the multi-attribute and/or multivariate analysis may include a principal component analysis. The principal component analysis may be effective to identify those attributes of the data reservoir that are most significant with respect to a given condition. Additionally or alternatively, in some embodiment, the multi-attribute and/or multivariate analysis may include the generation of one or more self-organizing maps (SOMs). The self-organizing map is an artificial neural network based machine-learning process that can be applied to 3D volumes, such as the data reservoir, to further classify data within that volume.

Applying the Data Analysis to the Target Reservoir

In some embodiments, the method may generally include applying the analysis of the well stages of the validated priority group to the target reservoir. For example, in the embodiment of FIG. 1, the method 100 includes applying the analysis of the well stages of the validated priority group to the target reservoir, as illustrated in step 114.

In some embodiments, applying the analysis of the well stages of the validated priority group to the target reservoir may include generating a 3D model of the target reservoir. For example, the analysis of the data reservoir, such as the relationships, trends, correlations and the like developed from a principal component analysis, self-organizing maps, or other multi-attribute/multivariate analysis developed based upon the data reservoir may be applied to the target reservoir. The 3D model of the target reservoir may incorporate available data associated with the target reservoir, such as three dimensional seismic data, ambient seismic data, passive seismic data, or other suitable data (such as from the water-hammer effect). Based upon this 3D model of the target reservoir, the method may yield a prediction of an effective conductivity of a target reservoir due to natural fracture systems or paths of weakness.

For example, in some embodiments, the methods may yield a qualitative prediction of the effective conductivity of various portions of the target reservoir. For example, various portions of the target reservoir may be characterized as having a “high” conductivity value, an “intermediate” conductivity value, or a “low” conductivity value. Additionally or alternatively, the methods may yield a quantitative prediction of the effective conductivity of the target reservoir. For example, in some embodiments, the predicted conductive value of two or more portions of the target reservoir may quantified, for example, for comparison. Additionally or alternatively, the methods can further quantify relative predictions between multiple target reservoirs to further distinguish predicted valuations.

In some embodiments, the methods may use the effective pre-fracture conductivity value to determine optimum locations for drilling in the target reservoir. Once again, this effective pre-fracture conductivity can be combined with seismic, or other data for a prospective site. In the event that diagnostic testing has occurred at the prospective site, this data can be incorporated to accurately predict conductivity of the prospective reservoir.

In some embodiments, the method may use the effective post-fracture conductivity value to determine optimum locations for drilling in a target reservoir. The method can make use of seismic, or other data for a prospective site in conjunction with effective conductivity values from similar well stages to predict an optimum drilling site for a future well.

In some embodiments, the method may include comparing the predicted results using both the pre-fracture and the post-fracture data. This allows for very specific validation of data utilized to predict conductivity and can result in a high degree of accuracy. Other data sources used for comparison and validation purposes including, but not limited to are: microseismic data, fiber optic data, distributed acoustic monitoring, distributed strain monitoring, distributed temperature monitoring, production rates, pressure data, and the like.

In some embodiments, the 3D model of the target reservoir may be used to predict an optimum location for a production or other well. For example, the predicted effective conductivity of various portions of the target reservoir can be used to locate those portions of the target reservoir that are most likely to be productive. Not intending to be bound by theory, portions of the reservoir qualitatively characterized as having high conductivity and/or quantitatively predicted to have relatively high conductivity may be advantageously selected for production. For example, relatively higher conductivity resulting from relatively higher occurrence of natural fracture systems or paths of weakness may allow improved fluid communication to a wellbore from the reservoir and, thus, improved productivity and/or decreased inputs. For example, and not intending to be bound by theory, by exploiting reservoirs and/or portions of a reservoir having a relatively high occurence of natural fractures, the opportunity for fluid connection between the wellbore and surface area within the reservoir may be improved. Therefore, by placing a well within a reservoir and/or a portion of a reservoir having a relatively high occurence of natural fractures and planes of weakness, upon hydraulically fracture stimulating the reservoir, access may be to the surface associated with the natural fractures. For example, FIG. 8 is a three-dimensional representation of the data reservoir illustrated in FIGS. 2, 4, 5, 6, and 7, where the data reservoir is also the target reservoir. The example of FIG. 8 illustrates the placement of potential wells 810 within the target reservoir so as to penetrate portions of the target reservoir having a relatively high occurence of natural fractures and planes of weakness.

In various embodiments, the predicted effective conductivity of various portions of the target reservoir can be used to select optimum well locations, select optimum well parameters, such as well coordinates, well orientation, well depth, number of well stages or zones, stimulation design and placement, and the like. For example, in a reservoir found to have a layer of rock that has very low degree of natural fracturing, the lack of natural fracturing may act as a barrier to hydraulic fracture stimulation, which in turn may limit the amount of reservoir surface area to which fluid connectivity can be provided. As a result, a horizontal well may be placed so as to avoid the layer of rock, for example, either well above or below this barrier. In another example, in a reservoir found to have a relatively high degree of natural fracturing, the spacing between wells (e.g., between horizontal portions of a well) may be increased such that it is necessary to drill fewer wells to develop the same amount of oil and gas. Conversely, in a reservoir having relatively little natural fracturing, the spacing between wells may be decreased, for example, such that more wells are placed within reservoir to develop the reservoir. As such, the number and placement of wells used can be made more efficient.

Additionally or alternatively, in some embodiments the predicted effective conductivity of various portions of the target reservoir can be used to further inform determinations as to existing wells, for example, such as whether to workover a well, whether to provide stimulation treatments, whether to relocate production zones, or the like. For example, a target reservoir may sits within the data reservoir and/or for existing horizontal wells within the data reservoir. As an example, an existing horizontal well that has, historically, not produced much oil and/or gas but is found to have been placed within a reservoir that having relatively high effective conduciting may justify stimulation operations to significantly increase the value of the well.

In some embodiments, the methods disclose herein may further comprise placing a well according to the predicted, optimum location. In such embodiments, a well may be drilled using a drilling rig positioned on the earth's surface and extending over and around a wellbore that penetrates reservoir for the purpose of recovering hydrocarbons. The well may be drilled into the reservoir using any suitable drilling technique. In some embodiments, the drilling rig comprises a derrick with a rig floor through which a work string extends downward from the drilling rig into the well.

The well may have any suitable characteristics, according the designs developed according to the disclosed methods. For example, the well may be horizontal or vertical, for example, extending substantially vertically away from the earth's surface over a vertical wellbore portion, or may deviate at any angle from the earth's surface over a deviated or horizontal wellbore portion. In various embodiments, portions or substantially all of the well may be vertical, deviated, horizontal, and/or curved. In some instances, at least a portion of the well may be lined with a casing that is secured into position against the reservoir in a conventional manner using cement, or may include portions that are uncased.

In some embodiments, the methods disclosed herein may be particularly advantageous in that these methods are able to accurately predict effective conductivity values with very little data. For example, in many instances less than five minutes of monitoring data can be utilized to predict an effective conductivity. While many tests or data collection methods may take days or weeks to complete in order to calculate actual reservoir parameters, the present embodiments may utilize a very small slice of the data to predict the effective conductivity of a reservoir.

While the above method has been described primarily as applicable to predicting an effective conductivity, persons of ordinary skill in the art, upon viewing this disclosure, will recognize that other factors can be incorporated to heighten the predictive value of the method. For example, characteristics affecting the extractable amount of oil and gas within a target reservoir can be included to optimize drilling locations.

Further, the above method can be used to quantify an amount of depletion in a target reservoir. The quantity of pressure depletion in a reservoir is a key characteristic affecting drilling decisions.

Below are examples of a practical application of the present embodiments:

EXAMPLE 1

In the Jonah field Section 35 a group of 48 wells were selected, drilled, and completed in the Mesaverde formation within the central fault block. No pre-fracture diagnostic testing was evaluated for any of these well stages. The fracture stimulation stages within each well was located from 13,100 feet to 13,400 feet, and were then designated as the priority group within the data reservoir to be evaluated.

The stages were then reviewed to determine the validity of the data to ensure they could be compared relative to one another. Three stages were eliminated from the priority group due to screen outs. One stage was eliminated because the pressure gauge readings appeared to be erratic and therefore suspected to be the result of equipment malfunctioning. Four other stages were eliminated since over 8 pounds per gallon of proppant was present within the fracture stimulation fluid at the end of the job as compared to a typical job, which ends with approximately 2 pounds per gallon proppant concentration.

The instantaneous shut in pressure was evaluated for each stage and based on this data it was determined that all of the stages had a similar reservoir pressure. Therefore a total of 40 wells made up the validated priority group of well stages to be analyzed. A dimensionlessly scaled analysis was conducted on the stimulation data for each stage. The rate of change of the dimensionless data was then calculated from the 0.01 and 0.03 dimensionless time scale.

The rate of change of the leak off pressure versus time plot was simultaneously calculated for the same data points used and then compared to the rate of change calculated from the dimensionless data analysis. The comparative analysis correlated without identifying any outlier data points.

The dimensionless data leak off rate data was then organized into a statistical distribution with 26 data points falling within one standard deviation of the mean and designated as having a medium effective post-fracture conductivity value. Eight data points were plotted in the lower quartile and were predicted to have a low effective post-fracture conductivity and the remaining six data points were located within the upper quartile and were designated to have high effective post fracture conductivity values.

The effective post fracture conductivity (kh) values for each well stage were then located within a 3D earth model of the data reservoir. The 3D earth model contained production logs, tracer surveys, electric line log measurements, drill mud log data, total organic content measurements from drill cuttings analyses, and high resolution whole core analyses on certain wells. The 3D earth model was then loaded into a 3D seismic interpretation project as a series of overlays within 3D space. A principal component analysis was then completed to identify significant seismic attributes that correspond to the earth model data set, including the effective conductivity (kh) values.

Multi-attribute and multivariate regression self organizing 3D volumes were then created that qualitatively illustrate the data reservoir. The 3D volumes were then compared on a qualitative basis with actual well production and the available production logs and tracer surveys plotted in 3D space. The comparison correlated very well whereas higher quality reservoir volumes corresponded directly with greater stage and overall well production of reservoir fluids.

The multi-attribute multivariate constructed 3D seismic volume described above was then used to predict higher quality 3D volumes within a target reservoir located in Section 36 of the Jonah field. The target reservoir is adjacent to the data reservoir evaluated. Wells were then located and drilled in Section 36 within the higher quality reservoir volumes which demonstrated higher effective conductivity reservoir rock. The new drilled wells performed within the top quartile of well production performances within the field as a whole. The new wells significantly improved the project economics through greater well productivity and the elimination of two wells that no longer needed to be drilled, which also reduced the environmental foot print in the field.

EXAMPLE 2

The validated priority group of stages selected in Section 35 of the Jonah field as discussed above were then evaluated based on diagnostic test data gathered on ten of the 40 stages in the data reservoir before they were fracture stimulated. The diagnostic test data set included six diagnostic injection formation tests (DFIT) and ten fluid efficiency tests (FET).

The instantaneous shut in pressure (ISIP) for the DFIT tests were plotted and compared to the FET ISIP data for each specific well stage. The comparison plotted as a straight line with an approximate slope of one. This further validated that the same reservoir was being compared during both types of tests, which were completed approximately one month apart for each applicable stage. A dimensionless data analysis was then conducted on all of the diagnostic test data for each stage. Reservoir pressure and reservoir conductivity (kh) were then deterministically calculated for each well stage that had DFIT data, which is a requirement for these calculations.

The rate of change of the dimensionless data curve was then calculated from the 0.01 and 0.03 dimensionless time for both the DFIT and FET tests. The rate of change of the leak off pressure versus time plot was simultaneously calculated for the same diagnostic test data points and then compared to the rate of change calculated from the dimensionless data analysis. The comparative analysis correlated without identifying any outlier data points. The rate of change for both the dimensionless data and the pressure time data sets were then cross plotted and formed approximately a straight line with a slope of one.

This further validated that the early time data for the DFIT and FET diagnostic tests could be used as a single data set. The DFIT rate of change from the dimensionless data analyses of well stages was then plotted versus the calculated reservoir conductivity from the DFIT test. A best fit curve was then plotted within this data set and then described by a polynomial equation that solved for the reservoir conductivity axis of the plot. The dimensionless data rate of change of the FET data was then inputted into the polynomial equation to solve for conductivity.

Since FET data was used in the equation we refer to the solution as an effective pre-fracture conductivity value. The calculated effective pre-fracture conductivity values from all of the FET tests were then plotted versus their respective dimensionless data rate of change values. The best fit curve was then adjusted slightly upwards to better fit the entire data set and the polynomial equation describing the adjusted curve was solved for effective pre-fracture conductivity. The same analysis of the pressure versus time data was completed and compared to the dimensionless data analysis. Both the dimensionless data and the pressure versus time analysis correlated but the dimensionless data analysis proved to have better resolution of the data and subsequently the calculated effective pre-fracture conductivity values,

It was therefore decided to only integrate the dimensionless data predicted effective pre-fracture conductivity values into the 3D earth model and seismic project described in Example 1, A principal component analysis was then repeated to identify significant seismic attributes that correspond to the earth model data set, including the effective pre-fracture conductivity (kh) values. Multi-attribute and multivariate regression self organizing 3D volumes were then recreated for the data reservoir.

The improved volumes illustrated greater resolution after the effective pre-fracture conductivity values were included, since these were discrete values rather than the high, medium, or low conductivity values described by the effective post-fracture conductivity methodology. The greater resolution of reservoir quality was evident in that it improved the match between reservoir quality and well production as measured with production logs and tracer surveys. The multi-attribute multivariate constructed 3D seismic volume described above was then used to predict with greater resolution the higher quality 3D volumes within a target reservoir located in Section 36 of the Jonah field.

Additional wells were then more precisely located and drilled in Section 36 within the higher quality reservoir volumes. The new drilled wells are predicted to perform ten percent better than the wells located with just post-fracture data as described in Example 1.

EXAMPLE 3

The validated priority group of stages selected in Section 35 of the Jonah field as discussed above were then evaluated using both the post-fracture and pre-fracture data. The instantaneous shut in pressure (ISIP) for the pre-fracture diagnostic tests were plotted and compared to the post-fracture ISIP data for each specific well stage. The comparison plotted as a straight line with a slope of approximately one.

The 1:1 plot confirmed that the same polynomial equation describing the best fit curve solving for effective conductivity (developed with pre-fracture data) can be used with the rate of change calculated from the dimensionless data analysis of the post-fracture data. Therefore, the post-fracture dimensionless data rate of change calculated in Example 1 was inserted into the polynomial equation and an effective post-fracture conductivity value was calculated for each well stage in the validated priority group.

The 3D earth model was then repopulated with only the dimensionless data predicted effective conductivity values from both the pre-fracture and post-fracture data sets for each of the applicable well stages. The revised 3D earth model was then reloaded into our 3D seismic interpretation project as a series of overlays within 3D space. A principal component analysis was then completed to identify significant seismic attributes that correspond to the earth model data set, including the effective conductivity (kh) values. Multi-attribute and multivariate regression self organizing 3D volumes were then created that qualitatively illustrate the data reservoir.

The 3D volumes were then compared on a qualitative basis with actual well production and the available production logs and tracer surveys plotted in 3D space. The comparison correlated even closer than the previous models. The improved volumes illustrated even greater resolution since the effective post-fracture conductivity values were now calculated using the polynomial effective conductivity equation rather than the high, medium, or low conductivity values described by the effective post-fracture conductivity methodology.

The revised and improved multi-attribute multivariate constructed 3D seismic volume described above was then used to predict with greater resolution the higher quality 3D volumes within a target reservoir located in Section 36 of the Jonah field. Additional wells were then even more precisely located and drilled in the target reservoir within the highest quality reservoir volumes. The volumes defined the most economically attractive reservoir. The new drilled wells are predicted to perform an additional 30 percent better than the wells located with just post-fracture methodology described.

While the present subject matter has been described with emphasis on the embodiments, it should be understood that within the scope of the appended claims, the embodiments might be practiced other than as specifically described herein.

Claims

1. A method of drilling a wellbore comprising:

collecting data for a plurality of well stages in at least one data reservoir;

creating a priority group of well stages from the plurality of well stages in the at least one data reservoir;

validating the well stages included within the priority group to create a validated priority group;

analyzing the data for the validated priority group to determine an effective conductivity value for each well stage in the validated priority group;

using the effective conductivity value to determine a target location for drilling the wellbore in the target reservoir; and

drilling a well at the target location in the target reservoir.

2. The method of claim 1, wherein validating the well stages included within the priority group comprises:

identifying each well stage in the plurality of well stages which has experienced a screen out or has experienced mechanical problems; or

identifying a stimulation design for each well stage in the plurality of well stages.

3. The method of claim 1, wherein analyzing the data for the validated priority group includes analyzing at least one of:

a breakdown pressure;

a clean fluid volume (fluid without proppant);

a conductivity;

a density;

a downhole pressure;

a downhole rate of fluid;

an effective conductivity;

a fracture breakdown pressure;

a fracture closure pressure;

a fracturing fluid volume;

an initial shut in pressure;

pounds of proppant pumped;

a proppant concentration;

a pump pressure;

a pump rate;

a reservoir fluid composition;

reservoir fluid properties;

a reservoir pressure;

a specific gravity;

a temperature of fluid; and

a time.

4. The method of claim 1, further comprising

collecting production data for each well stage of the plurality of well stages in the at least one data reservoir;

comparing the production data to the effective conductivity value for each well stage of the plurality of well stages in the at least one data reservoir; and

verifying that the effective conductivity value correlates with the production data.

5. The method of claim 1, wherein validating the well stages included within the priority group comprises: identifying, evaluating, and selectively eliminating outliers.

6. The method of claim 5, wherein validating the well stages included within the priority group comprises: eliminating well stages that experienced a screen out, well stages that experienced mechanical problems, or the well stages with dissimilar stimulation designs.

7. The method of claim 1, wherein the validated priority group of the well stages comprises well stages with similar data.

8. The method of claim 1, wherein analyzing the data for the validated priority group comprises:

calculating a pressure leak-off rate for each well stage in the validated priority group; and

determining the effective conductivity value for each well stage in the validated priority group based upon the pressure leak-off rate of each well stage in the priority group.

9. The method of claim 1, wherein analyzing the data for the validated priority group comprises applying a dimensionless scaling factor to the data to form dimensionless data.

10. The method of claim 9, wherein analyzing the data for the validated priority group comprises determining an effective dimensionless conductivity value for each well stage in the validated priority group based upon the dimensionless data.

11. The method of claim 10, wherein analyzing the data comparing the effective dimensionless conductivity value to the effective conductivity value.

12. A method of locating a wellbore comprising:

collecting data for a plurality of well stages in at least one data reservoir;

creating a priority group of well stages from the plurality of well stages in the at least one data reservoir;

validating the well stages included within the priority group to create a validated priority group;

analyzing the data for the validated priority group to determine an effective conductivity value for each well stage in the validated priority group; and

using the effective conductivity value to determine a target location for drilling the wellbore in the target reservoir.

13. The method of claim 12, wherein analyzing the data for the validated priority group comprises determining the effective conductivity value for each well stage in the priority group based upon the data of each well stage in the priority group.

14. The method of claim 12, wherein analyzing the data for the validated priority group comprises applying a dimensionless scaling factor to the data to form dimensionless data.

15. The method of claim 14, wherein analyzing the data for the validated priority group comprises determining an effective dimensionless conductivity value for each well stage in the validated priority group based upon the dimensionless data.

16. The method of claim 15, wherein analyzing the data for the validated priority group comprises comparing the effective dimensionless conductivity value to the effective conductivity value.

17. A method of locating a wellbore in a target reservoir comprising:

establishing a model of a data reservoir in a validated priority group by: collecting data for a plurality of well stages in the data reservoir; creating a priority group of well stages from the plurality of well stages in the data reservoir; validating the well stages included within the priority group to create the validated priority group; analyzing the data for the validated priority group to determine the effective conductivity value for each well stage in the validated priority group; developing the model of the data reservoir using the effective conductivity value for each well stage in the validated priority group;

predicting an effective conductivity value in the target reservoir using the model of the data reservoir to; and

locating the wellbore in the target reservoir using the predicted effective conductivity in the target reservoir.

18. The method of claim 17, wherein analyzing the data for the validated priority group comprises applying a dimensionless scaling factor to the data to form dimensionless data.

19. The method of claim 18, wherein analyzing the data for the validated priority group comprises determining an effective dimensionless conductivity value for each well stage in the validated priority group based upon the dimensionless data.

20. The method of claim 17, wherein predicting the effective conductivity value in the target reservoir comprises correlating at least one attribute associated with the target reservoir with the to the predicted effective conductivity value.