US-12625289-B2 - Determining well productivity for hydraulically fractured wells

Abstract

Methods and systems for determining well productivity include acquiring measurement data from a plurality of in-situ sensors located within a hydraulically fractured subterranean formation; classifying the noise degree for sensors of the plurality based on the acquired measurement data; selecting sensors from the plurality by minimizing noise degree while maintaining coverage of the subterranean formation above a user defined threshold; extracting data from the selected sensors; and estimating fracture half-length and well productivity potential based on the extracted data.

Inventors

• Abdallah A. Alshehri
• Klemens Katterbauer

Assignees

• SAUDI ARABIAN OIL COMPANY

Dates

Publication Date

20260512

Application Date

20230214

Claims (20)

1. 1 . A method for determining well productivity, the method comprising: acquiring measurement data from a plurality of in-situ sensors located within fractures in a hydraulically fractured well in a subterranean formation; classifying a noise degree for sensors of the plurality, the noise degree representing a quality of the acquired measurement data from the sensors of the plurality; reducing effects of noise in the measurement data while maintaining coverage of the fractures above a threshold by selecting sensors from the plurality based on the noise degree classification and based on a physical distribution of the in-situ sensors in the fractures; extracting data from the selected sensors having the physical distribution and having the noise degree satisfying the threshold; and estimating fracture half-length and well productivity potential based on the extracted data.
2. 2 . The method of claim 1 , wherein the minimizing comprises a mixed-integer programming framework.
3. 3 . The method of claim 1 , further comprising: filtering measurement data from the plurality of in-situ sensors to remove noise from the measurement data, the filtering comprising artificial intelligence (AI) window filtering that is based on a radial basis function neural network.
4. 4 . The method of claim 1 , further comprising: accessing, from a data store, data comprising at least one of rock property data, hydraulic fracturing parameter data, and sensor location data; combining the accessed data with the extracted data from the selected sensors; and estimating fracture half-length and well productivity potential based on the combined data.
5. 5 . The method of claim 1 , further comprising: estimating a well productivity based on at least one of the estimated fracture half-length and well productivity potential, wherein the estimating comprises a decline curve analysis.
6. 6 . The method of claim 5 , wherein the decline curve analysis comprises a long short-term memory framework to predict declines in production based on a time-series of well productivity.
7. 7 . The method of claim 5 , further comprising: monitoring a production well to determine well leakage based on the estimated well productivity and the measured production.
8. 8 . The method of claim 1 , wherein estimating fracture half-length and well productivity potential is based on a pretrained XGBoost machine learning model.
9. 9 . The method of claim 1 , wherein the measurement data include at least one of temperature, pressure, and chemical concentration.
10. 10 . A system for estimating well productivity, the system comprising: a plurality of in-situ sensors; a base station; at least one processor; and a memory storing instructions that when executed by the at least one processor cause the at least one processor to perform operations comprising: acquiring measurement data from the plurality of in-situ sensors located within fractures in a hydraulically fractured well in a subterranean formation; classifying a noise degree for sensors of the plurality, the noise degree representing a quality of the acquired measurement data from the sensors of the plurality; reducing effects of noise in the measurement data while maintaining coverage of the fractures above a threshold by selecting sensors from the plurality based on the noise degree classification and based on a physical distribution of the in-situ sensors in the fractures; extracting data from the selected sensors having the physical distribution and having the noise degree satisfying the threshold; and estimating fracture half-length and well productivity potential based on the extracted data.
11. 11 . The system of claim 10 , wherein the in-situ sensors comprise an energy harvesting module to harvest vibrational energy.
12. 12 . The system of claim 10 , wherein the in-situ sensors induce vibrations within fractures of the subterranean formation larger than microseismic events emitted by the fracture to improve detectability of microseismic events by geophones.
13. 13 . The system of claim 10 , the operations further comprising: filtering measurement data from the plurality of in-situ sensors to remove noise from the measurement data, the filtering comprising artificial intelligence (AI) window filtering that is based on a radial basis function neural network.
14. 14 . The system of claim 10 , the operations further comprising: estimating a well productivity based on at least one of the estimated fracture half-length and well productivity potential, wherein the estimating comprises a decline curve analysis comprising a long short-term memory framework to predict declines in production based on a time-series of well productivity.
15. 15 . The system of claim 10 , wherein estimating fracture half-length and well productivity potential is based on a pretrained XGBoost machine learning model.
16. 16 . One or more non-transitory machine-readable storage devices storing instructions for determining well productivity, the instructions being executable by one or more processing devices to cause performance of operations comprising: acquiring measurement data from a plurality of in-situ sensors located within fractures in a hydraulically fractured well in a subterranean formation; classifying a noise degree for sensors of the plurality, the noise degree representing a quality of the acquired measurement data from the sensors of the plurality; reducing effects of noise in the measurement data while maintaining coverage of the fractures above a threshold by selecting sensors from the plurality based on the noise degree classification and based on a physical distribution of the in-situ sensors in the fractures; extracting data from the selected sensors having the physical distribution and having the noise degree satisfying the threshold; and estimating fracture half-length and well productivity potential based on the extracted data.
17. 17 . The non-transitory machine-readable storage devices of claim 16 , the operations further comprising: filtering measurement data from the plurality of in-situ sensors to remove noise from the measurement data, the filtering comprising artificial intelligence (AI) window filtering that is based on a radial basis function neural network.
18. 18 . The non-transitory machine-readable storage devices of claim 16 , the operations further comprising: estimating a well productivity based on at least one of the estimated fracture half-length and well productivity potential, wherein the estimating comprises a decline curve analysis comprising a long short-term memory framework to predict declines in production based on a time-series of well productivity.
19. 19 . The non-transitory machine-readable storage devices of claim 18 , the operations further comprising: monitoring a production well to determine well leakage based on the estimated well productivity and the measured production.
20. 20 . The non-transitory machine-readable storage devices of claim 16 , wherein estimating fracture half-length and well productivity potential is based on a pretrained XGBoost machine learning model.

Description

TECHNICAL FIELD The present disclosure generally relates to hydraulically fractured wells. BACKGROUND Hydraulic fracturing is a well completion operation used to crack reservoir formations via injection of high-pressure water to prepare the well for production and improve hydrocarbons flow to the wellbore especially from low permeability formations. Once a certain formation is fractured, proppants are pumped into these fractures to keep them open after dropping the fluid pressure. Current methods of hydraulic fracturing monitoring, such as micro-seismic monitoring, employ a set of seismic sensors on the surface or in neighboring wellbores where signal noise can mask small magnitude seismic signals generated by fractures. Another setup employs seismic sensors in neighboring monitoring wells or laterals to reduce the effects of noise and improve signal-to-noise ratios; however, monitoring wells or laterals may not be available. SUMMARY This specification describes techniques for determining well productivity in hydraulically fractured wells. In-situ sensing devices of micrometer to millimeter size are pumped into fractures alongside proppants to monitor fracture extent and direction and to induce larger in-situ vibrations that can be better detected by seismic sensors. These devices can be energized by a vibration source from the surface or the borehole and then activated to vibrate and act as micro-seismic sources within the fractures. The data gathered from the sensors is integrated into a deep learning framework to determine the well productivity. In one aspect a method for determining well productivity includes acquiring measurement data from a plurality of in-situ sensors located within a hydraulically fractured subterranean formation; classifying the noise degree for sensors of the plurality based on the acquired measurement data; selecting sensors from the plurality by minimizing noise degree while maintaining coverage of the subterranean formation above a user defined threshold; extracting data from the selected sensors; and estimating fracture half-length and well productivity potential based on the extracted data. In one aspect, a system for estimating well productivity includes a plurality of in-situ sensors; a base station; at least one processor; and a memory storing instructions that when executed by the at least one processor cause the at least one processor to perform operations including acquiring measurement data from a plurality of in-situ sensors located within a hydraulically fractured subterranean formation; classifying the noise degree for sensors of the plurality based on the acquired measurement data; selecting sensors from the plurality by minimizing noise degree while maintaining coverage of the subterranean formation above a user defined threshold; extracting data from the selected sensors; and estimating fracture half-length and well productivity potential based on the extracted data. In one aspect, one or more non-transitory machine-readable storage devices storing instructions for determining well productivity, the instructions being executable by one or more processing devices to cause performance of operations including acquiring measurement data from a plurality of in-situ sensors located within a hydraulically fractured subterranean formation; classifying the noise degree for sensors of the plurality based on the acquired measurement data; selecting sensors from the plurality by minimizing noise degree while maintaining coverage of the subterranean formation above a user defined threshold; extracting data from the selected sensors; and estimating fracture half-length and well productivity potential based on the extracted data. Embodiments of these systems and methods can include one or more of the following features. In some embodiments, the minimizing includes a mixed-integer programming framework. In some embodiments, these aspects further include filtering measurement data from the plurality of in-situ sensors to remove noise from the measurement data, the filtering comprising artificial intelligence (AI) window filtering that is based on a radial basis function neural network. In some embodiments, these aspects further include accessing, from a data store, data including at least one of rock property data, hydraulic fracturing parameter data, and sensor location data; combining the accessed data with the extracted data from the selected sensors; and estimating fracture half-length and well productivity potential based on the combined data. In some embodiments, these aspects further include estimating a well productivity based on at least one of the estimated fracture half-length and well productivity potential, wherein the estimating includes a decline curve analysis. In some cases, the decline curve analysis includes a long short-term memory framework to predict declines in production based on a time-series of well productivity. In some embodiments, these aspects furthe