AR114779A1 - PREDICTIONS IN NON-CONVENTIONAL RESERVOIRS THROUGH THE USE OF MACHINE LEARNING - Google Patents
PREDICTIONS IN NON-CONVENTIONAL RESERVOIRS THROUGH THE USE OF MACHINE LEARNINGInfo
- Publication number
- AR114779A1 AR114779A1 ARP190100970A ARP190100970A AR114779A1 AR 114779 A1 AR114779 A1 AR 114779A1 AR P190100970 A ARP190100970 A AR P190100970A AR P190100970 A ARP190100970 A AR P190100970A AR 114779 A1 AR114779 A1 AR 114779A1
- Authority
- AR
- Argentina
- Prior art keywords
- parameters
- production
- subset
- new well
- data values
- Prior art date
Links
- 238000010801 machine learning Methods 0.000 title abstract 2
- 238000000034 method Methods 0.000 abstract 4
- 238000005553 drilling Methods 0.000 abstract 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
- G06N20/20—Ensemble learning
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Software Systems (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physics & Mathematics (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Artificial Intelligence (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
En algunos ejemplos, un método para planificar un nuevo pozo en un yacimiento no convencional comprende obtener valores de datos para una pluralidad de parámetros de una pluralidad de pozos que están dentro de una distancia meta de una ubicación meta del nuevo pozo, pluralidad de parámetros que incluye uno o más parámetros de producción para el uno o más pozos. El método comprende identificar un subconjunto de parámetros entre la pluralidad de parámetro usando los valores de datos y un algoritmo de aprendizaje automático, subconjunto de parámetros que incluye el uno o más parámetros de producción y aquellos de la pluralidad de parámetros con influencia sobre el uno o más parámetros de producción que supera un umbral. El método también comprende producir un modelo aplicando los valores de datos correspondientes al subconjunto de parámetros al algoritmo, modelo que describe relaciones entre parámetros en el subconjunto. El método también incluye predecir la producción del nuevo pozo usando el modelo y perforar el nuevo pozo en la ubicación meta o en otra ubicación en base a la producción que se predijo.In some examples, a method of planning a new well in an unconventional reservoir comprises obtaining data values for a plurality of parameters from a plurality of wells that are within a target distance of a target location of the new well, which plurality of parameters includes one or more production parameters for the one or more wells. The method comprises identifying a subset of parameters among the plurality of parameters using the data values and a machine learning algorithm, subset of parameters including the one or more production parameters and those of the plurality of parameters influencing the one or more production parameters than exceeds a threshold. The method also comprises producing a model by applying the data values corresponding to the subset of parameters to the algorithm, which model describes relationships between parameters in the subset. The method also includes predicting production from the new well using the model and drilling the new well at the target location or another location based on the production that was predicted.
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US201862656893P | 2018-04-12 | 2018-04-12 |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| AR114779A1 true AR114779A1 (en) | 2020-10-14 |
Family
ID=66323920
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| ARP190100970A AR114779A1 (en) | 2018-04-12 | 2019-04-12 | PREDICTIONS IN NON-CONVENTIONAL RESERVOIRS THROUGH THE USE OF MACHINE LEARNING |
Country Status (2)
| Country | Link |
|---|---|
| AR (1) | AR114779A1 (en) |
| WO (1) | WO2019199723A1 (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11922104B2 (en) * | 2021-01-15 | 2024-03-05 | Saudi Arabian Oil Company | Predicting oil gains derived from horizontal sidetracking of producer wells using past production performance, subsurface information, and sidetrack design parameters |
| WO2023281287A1 (en) | 2021-07-08 | 2023-01-12 | Totalenergies Onetech | A method for predicting the time evolution of a parameter for a set of wells |
| CN115539026B (en) * | 2022-09-27 | 2023-11-14 | 西南石油大学 | Initial yield fusion prediction method for horizontal well of complex reservoir |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8504341B2 (en) * | 2006-01-31 | 2013-08-06 | Landmark Graphics Corporation | Methods, systems, and computer readable media for fast updating of oil and gas field production models with physical and proxy simulators |
| CA2876266C (en) * | 2012-06-11 | 2018-10-23 | Landmark Graphics Corporation | Methods and related systems of building models and predicting operational outcomes of a drilling operation |
| US9262713B2 (en) * | 2012-09-05 | 2016-02-16 | Carbo Ceramics Inc. | Wellbore completion and hydraulic fracturing optimization methods and associated systems |
-
2019
- 2019-04-09 WO PCT/US2019/026456 patent/WO2019199723A1/en active Application Filing
- 2019-04-12 AR ARP190100970A patent/AR114779A1/en unknown
Also Published As
| Publication number | Publication date |
|---|---|
| WO2019199723A1 (en) | 2019-10-17 |
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| FB | Suspension of granting procedure |