RELATED APPLICATIONS

The disclosures of “Systems And Methods For Estimating Reservoir Productivity Values As A Function of Depth In A Subsurface Volume Of Interest” U.S. patent application Ser. No. 16/144,736, filed Sep. 27, 2018, “Systems And Methods For Estimating Reservoir Productivity As A Function of Position In A Subsurface Volume Of Interest” U.S. patent application Ser. No. 16/144,815, filed Sep. 27, 2018, and “Systems And Methods For Estimating A Well Design Reservoir Productivity As A Function of Position In A Subsurface Volume Of Interest Based On A Reservoir Productivity Parameter” U.S. patent application Ser. No. 16/281,018, filed Feb. 20, 2019 are hereby incorporated by reference herein.

FIELD OF THE DISCLOSURE

The present disclosure relates to systems and methods for estimating refined reservoir productivity values as a function of position in a subsurface volume of interest based on a refined well design.

SUMMARY

Implementations of the disclosure are directed to systems and methods for estimating reservoir productivity values that maximize reservoir productivity of a refined well design corresponding to a reservoir productivity parameter. In one implementation, a computer-implemented method may include a computer system. The computer system may include a physical computer processor and non-transient electronic storage. The computer-implemented method may include obtaining multiple limited production parameter values. The limited production parameter values may specify subsurface production features that affect the reservoir productivity. The computer-implemented method may also include obtaining multiple refined well designs. Individual ones of the multiple refined well designs may specify a design parameter value for a corresponding design parameter. The computer-implemented method may further include obtaining a productivity algorithm that predicts a reservoir productivity based on a production parameter and a well design. The computer-implemented method may also include generating multiple refined reservoir productivity values as a function of position in the subsurface volume of interest by applying the multiple refined well designs and the multiple limited production parameter values to the productivity algorithm.

In implementations, the productivity algorithm may be conditioned by training an initial productivity algorithm using productivity training data. The productivity training data may include (i) the production parameter values in the subsurface volume of interest, (ii) multiple sets of well designs in the subsurface volume of interest, and (iii) corresponding reservoir productivity values.

In implementations, the computer-implemented method may include a graphical user interface. The computer-implemented method may include generating a representation of refined reservoir productivity as a function of position in the subsurface volume of interest using visual effects to depict at least a portion of the refined reservoir productivity values. The computer-implemented method may also include displaying the representation.

In implementations, the computer-implemented method may include a graphical user interface. The computer-implemented method may include generating multiple completion efficiency values based on the refined reservoir productivity and the refined well design. The computer-implemented method may also include generating a representation of completion efficiency as a function of position in the subsurface volume of interest using visual effects to depict at least a portion of the multiple completion efficiency values. The computer-implemented method may further include displaying the representation.

In implementations, generating multiple completion efficiency values may include a ratio between the refined reservoir productivity values and corresponding well designs.

In implementations, the computer system may further include a graphical user interface. The computer-implemented method may include generating multiple recovery factor values. The computer-implemented method may also include generating a representation of recovery factor as a function of position in the subsurface volume of interest using visual effects to depict at least a portion of the multiple recovery factor values. The computer-implemented method may further include displaying the representation.

In implementations, generating multiple recovery factor values may be generated by performing a number of operations. One operation may include generating multiple storage volumes as a function of position in the subsurface volume of interest. Another operation may include generating the multiple recovery factor values based on the refined reservoir productivity and the storage volumes.

In implementations, the multiple refined well designs may be generated by performing a number of operations. One operation may include obtaining multiple well designs. Another operation may include generating predicted reservoir productivity values by applying the productivity algorithm to the multiple well designs. Another operation may include generating a best-fit function through a distribution of the multiple well designs and the predicted reservoir productivity values. Yet another operation may include parameterizing the best-fit function based on a reservoir productivity parameter.

In implementations, the reservoir productivity parameter may include one or more of a cost-value threshold value and a maximum recovery threshold value.

In implementations, the limited production parameter values may be generated by performing a number of operations. One operation may include obtaining subsurface data and well data corresponding to a subsurface volume of interest. Another operation may include obtaining a parameter model. Yet another operation may include generating multiple production parameter maps based on the subsurface data and the well data. Another operation may include generating limited production parameter values by applying the parameter model to the multiple production parameter maps.

In implementations, the parameter model may be conditioned by training an initial parameter model using training data. The training data may include (i) the well data of one or more wells in the subsurface volume of interest, and (ii) the production parameter values for corresponding multiple production parameters affecting productivity of the one or more wells as a function of position in the subsurface volume of interest.

In another implementation, a system is disclosed. The system may include non-transient electronic storage and a physical computer processor configured by machine-readable instructions to perform a number of operations. One operation may include obtaining multiple limited production parameter values. Another operation may include obtaining multiple refined well designs. Yet another operation may include obtaining a productivity algorithm that predicts a reservoir productivity based on a production parameter and a well design. Another operation may include generating multiple refined reservoir productivity values as a function of position in the subsurface volume of interest by applying the multiple refined well designs and multiple limited production parameter values to the productivity algorithm

In implementations, the system may further include a graphical user interface. One of the operations may include generating a representation of the refined reservoir productivity values as a function of position in the subsurface volume of interest using visual effects to depict at least a portion of the refined reservoir productivity values. Another operation may include displaying the representation.

In implementations, the system may further include a graphical user interface. One operation may include generating multiple completion efficiency values based on the refined reservoir productivity and the refined well design. Another operation may include generating a representation of completion efficiency as a function of position in the subsurface volume of interest using visual effects to depict at least a portion of the multiple completion efficiency values. One operation may include display the representation.

In implementations, the system may further include a graphical user interface. One operation may include generating multiple recovery factor values. Yet another operation may include generating a representation of recovery factor as a function of position in the subsurface volume of interest using visual effects to depict at least a portion of the multiple recovery factor values. One operation may include displaying the representation.

In implementations, generating multiple recovery factor values may include generating storage volumes as a function of position in the subsurface volume of interest. Generating multiple recovery factor values may also include generating the multiple recovery factor values based on the refined reservoir productivity and the storage volumes.

In implementations, the multiple refined well designs may be generated by a number of operations. One operation may include obtaining multiple well designs. Another operation may include generating predicted reservoir productivity values by applying the productivity algorithm to the multiple well designs. Yet another operation may include generating a best-fit function through a distribution of the multiple well designs and the predicted reservoir productivity values. Another operation may include parameterizing the best-fit function based on a reservoir productivity parameter.

In implementations, the reservoir productivity parameter may include one or more of a cost-value threshold value and a maximum recovery threshold value.

In yet another implementation, a computer-implemented method for training an initial productivity algorithm is disclosed. The computer-implemented method may include a physical computer processor and non-transient electronic storage. The computer-implemented method may include obtaining productivity training data. The productivity training data may include (i) the production parameter values in the subsurface volume of interest, (ii) multiple sets of well designs in the subsurface volume of interest, and (iii) corresponding reservoir productivity values The computer-implemented method may also include obtaining an initial productivity algorithm to predict a reservoir productivity based on a production parameter and a well design. The computer-implemented method may further include generating a productivity algorithm by conditioning the initial productivity algorithm using the productivity training data. The computer-implemented method may also include obtaining multiple limited production parameter values. The computer-implemented method may further include obtaining multiple refined well designs. The computer-implemented method may further include generating multiple refined reservoir productivity values as a function of position in the subsurface volume of interest by applying the multiple refined well designs and multiple limited production parameter values to the productivity algorithm

In implementations, a computer-implemented method may further include a graphical user interface. The computer-implemented method may include generating a representation of the refined reservoir productivity values as a function of position in the subsurface volume of interest using visual effects to depict at least a portion of the refined reservoir productivity values. The computer-implemented method may also include displaying the representation.

These and other features, and characteristics of the present technology, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended Claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the presently disclosed technology. As used in the specification and in the Claims, the singular form of “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise.

The technology disclosed herein, in accordance with one or more various implementations, is described in detail with reference to the following figures. The drawings are provided for purposes of illustration only and merely depict typical or example implementations of the disclosed technology. These drawings are provided to facilitate the reader's understanding of the disclosed technology and shall not be considered limiting of the breadth, scope, or applicability thereof. It should be noted that for clarity and ease of illustration these drawings are not necessarily made to scale.

