STREAMLINED FRAMEWORK FOR IDENTIFYING AND IMPLEMENTING FIELD DEVELOPMENT OPPORTUNITIES

• US 20190325331A1
• Filed: 04/19/2019
• Published: 10/24/2019
• Est. Priority Date: 04/20/2018
• Status: Active Application

• Current Assignee: QRI Group, LLC
• Original Assignee: QRI Group, LLC
• Inventors: Benhallam, Wassim, Salehi, Amir, Darabi, Hamed, Castineira, David, Close, Aaron D., Heil, Richard J.
• Application Number:
  US16/389,086
• Publication Number:
  US 20190325331A1
• Time in Patent Office: Days
• CPC Class Codes: E21B 43/00 Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells applicable only to water E03B
• CPC Class Codes: E21B 43/14 Obtaining from a multiple-zone well
• CPC Class Codes: E21B 47/00 Survey of boreholes or wells monitoring pressure or flow of drilling fluid E21B21/08
• CPC Class Codes: G01V 20/00 Geomodelling in general
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 5/048 Fuzzy inferencing
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202

Abstract

Embodiments are directed to identifying and implementing hydrocarbon production opportunities including recompletion opportunities, new vertical drill target opportunities, and horizontal or deviated well target opportunities. Computer systems access multi-disciplinary data, perform data validation and pre-processing on the data, identify field development opportunities by identifying candidates, forecasting production at those candidates, generating an uncertainty quantification, and vetting and validating the data. The computer systems then list the viable well target opportunities including recompletion opportunities, vertical new drill target opportunities and deviated and horizontal target opportunities.

Claims

1. A method, implemented at a computer system that includes at least one processor, for identifying and implementing hydrocarbon production opportunities including recompletion opportunities, the method comprising:

accessing one or more portions of petrophysical log data obtained at a hydrocarbon extraction site to generate a geological map of the site;

accessing one or more portions of historical completion data for the hydrocarbon extraction site to identify uncontacted net pay intervals, the uncontacted net pay intervals representing material remaining in one or more hydrocarbon wells on the hydrocarbon site;

analyzing isolated and connected intervals of the hydrocarbon wells to determine, according to the geological map, which portions of the hydrocarbon wells have been drained by existing completions;

forecasting, using statistical neighborhood methods and one or more machine learning algorithms, projected production results for one or more recompletion opportunities at the hydrocarbon site, the projected production results including initial production estimates and/or ultimate recovery estimates;

calculating a level of geologic uncertainty relative to the forecasted production results and determined drainage, wherein the geologic uncertainty levels indicate levels of risk;

filtering a list of recompletion opportunities for selection according to geological feasibility, mechanical feasibility and/or engineering feasibility; and

initiating hydrocarbon production at at least one of the recompletion opportunities.

2. The method of claim 1, further comprising accessing one or more wellbore diagrams of at least one of the hydrocarbon wells to mine additional information indicating which recompletion opportunities are feasible.

3. The method of claim 1, wherein vetted petrophysical interpretations are used to create a geological model that shows the spatial distribution of specified geological properties for each formation or zone.

4. The method of claim 1, wherein analyzing isolated and connected intervals of the hydrocarbon wells to determine intervals that have been drained by existing completions includes tracking net pay intervals that are in direct or indirect communication with existing perforations.

5. The method of claim 4, wherein the tracking of net pay intervals is customizable to account for baffle layers or other flow barriers, current fluid contacts, perforation strategy, and saturation data derived from recurrent measurements or from dynamic simulation models.

6. The method of claim 1, wherein analyzing isolated and connected intervals of the hydrocarbon wells to determine which portions of the hydrocarbon wells have been drained includes creating a drainage grid, each formation using the production volume or estimated ultimate recovery allocated to the formation in each hydrocarbon well.

7. The method of claim 1, wherein calculating the level of geologic uncertainty relative to the forecasted production results and determined drainage includes determining at least one of structural risks, mapping risks, petrophysical risks or saturation risks.

8. One or more computer-readable media that store computer-executable instructions that, when executed, implement a method for identifying and implementing hydrocarbon production opportunities including new vertical drill target opportunities, the method comprising:

accessing one or more portions of geological, petrophysical or engineering data related to a hydrocarbon extraction site;

analyzing the accessed data to identify one or more well placement grid cells in the hydrocarbon extraction site that are fit for placing new wells according to one or more well placement constraints;

generating a relative probability of success (RPOS) mapping for each zone that ranks the identified well placement grid cells according to one or more zone-mappable attributes associated with productive hydrocarbon wells;

forecasting, for each new drill location, a potential production rate for one or more target zones;

estimating the probability of exceeding or falling short of the forecasted potential production rate using at least a portion of neighborhood production data indicating an amount that wells in neighboring zones are producing;

determining, based on one or more geologic factors related to the hydrocarbon site, a geologic risk measurement for a given target opportunity, the risk measurement indicating the connectivity of the reservoir and the quality of the geological mapping;

filtering a list of new vertical drilling opportunities for selection according to geological feasibility, mechanical feasibility and/or engineering feasibility; and

initiating hydrocarbon production at the selected new vertical drilling opportunity.

9. The computer-readable media of claim 8, wherein analyzing the accessed data to identify one or more well placement zones in a hydrocarbon extraction site that are fit for placing new wells according to one or more well placement constraints comprises creating a Boolean spacing grid to perform the well placement analysis.

10. The computer-readable media of claim 8, further comprising performing a drainage analysis configured to analyze isolated and connected intervals of the hydrocarbon wells to determine which portions of the hydrocarbon wells have been drained by existing completions.

11. The computer-readable media of claim 8, further comprising generating a global relative probability of success (RPOS) map that aggregates the RPOS for each stratigraphic zone, each global RPOS map being generated using multiple spatial attributes that are known to affect production performance.

12. The computer-readable media of claim 11, further comprising selecting one or more sweet spots for placing new vertical wells that maximize the global RPOS while honoring the well placement constraints.

13. The computer-readable media of claim 8, wherein the geological uncertainty associated with each new drill location is quantified using algorithms that assess the uncertainty of structural interpretations and the geologic risk as indicated by the availability and distribution of control data points.

14. A computer system for identifying and implementing hydrocarbon production opportunities including horizontal or deviated well target opportunities, comprising:

one or more processors;

system memory;

a data accessing module configured to access one or more portions of geological, petrophysical or engineering data related to a hydrocarbon extraction site;

a domain identifier configured to identify potential regions that satisfy one or more stratigraphic, spatial and depth constraints within the hydrocarbon extraction site;

a spatial analyzer configured to perform a 3D spatial analysis of one or more of the identified potential well placement zones within the hydrocarbon extraction site;

a drainage analyzer configured to identify one or more zones of the hydrocarbon extraction site that include drained portions that have been drained by existing wells on the site;

a 3D map generator configured to generate a relative probability of success (RPOS) 3D map that ranks the identified zones of the site according to one or more zone-mappable attributes associated with productive hydrocarbon wells;

a well target identifier configured to search the 3D map to identify optimal target locations for horizontal or deviated wells according to the RPOS 3D map and one or more well placement constraints including at least one of azimuth, target length, or deviation range as defined by a user;

an optimization module configured to perform an interference analysis designed to filter through and select an optimal set of non-interfering well candidates;

a forecasting module configured to forecast an initial production rate for the selected horizontal well placement candidate placed in the identified location on the hydrocarbon extraction site using one or more analytical, simulation, or machine learning models; and

a production initiator configured to initiate hydrocarbon production at the selected horizontal well placement candidate in the identified location.

15. The system of claim 14, wherein the Boolean spacing grid includes three dimensional (3D) meshes wrapped around existing well trajectories with a given spacing parameter, resulting in a set of curved 3D cylinders that are used to create a model mask indicating which cells satisfy the spacing constraints.

16. The system of claim 14, wherein the RPOS 3D map comprises a 3D map of model properties that aggregates trends in multiple user-defined attributes, including those attributes that are known to impact hydrocarbon production.

17. The system of claim 14, wherein the well target identifier is configured to identify potential horizontal or deviated well targets using an optimized global search that incorporates a plurality of search constraints including well configuration constraints and path constraints.

18. The system of claim 14, wherein a pay tracking analysis using a recursive flood fill algorithm is performed on the identified targets to determine the vertical and lateral extent of pay cells in communication with the grid cells penetrated by the target.

19. The system of claim 14, wherein an interference analysis using a greedy algorithm is performed on the identified targets to select an optimal subset of non-interfering candidates that optimize a user-selected objective function.

20. The system of claim 14, wherein initiating hydrocarbon production at the selected horizontal well in the identified location comprises providing one or more control instructions that direct operation of the selected target.