An Effective Physics-Based Deep Learning Model for Enhancing Production Surveillance and Analysis in Unconventional Reservoirs

Pan, Yuewei; Bi, Ran; Zhou, Peng; Deng, Lichi; Lee, John

doi:10.15530/urtec-2019-145

Cited by 16 publications

(3 citation statements)

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“…Pan et al [108] applied a cascaded LSTM model, (tree-structured LSTM network) coupled with a Savitzky-Golay filter [109] for hydraulic fracture production prediction and monitoring purposes. The filter is a polynomial algorithm used to smooth time-series data.…”

Section: Dynamic Machine Learning Modelsmentioning

confidence: 99%

“…Dynamic predictions RNNs (mostly LSTMs) [20,44,54,66,[71][72][73]75,106,108] ANNs or ANNs with optimization techniques [22,76,77,79,86,87,107,135,150,153,169,171,182,183] RBFNN [153,170] SVMs and their variations (e.g., LSSVM, SVRs) [152] MTS and VAR [80] HONNs [84,85] Gamma regression [86,87] MLMVN [89] Chaotic Neural Networks [185] As can be observed from Table 1, a wide range of ML methods is readily available, with each one specialized to the problem under investigation. Artificial Neural Networks (ANNs) are mostly used for these applications, such as predicting production recovery factors and net present values, as well as optimizing well locations and design parameters to achieve the best possible production.…”

Section: Supervisedmentioning

confidence: 99%

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Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II

Samnioti,

Gaganis

2023

Energies

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In recent years, Machine Learning (ML) has become a buzzword in the petroleum industry, with numerous applications which guide engineers in better decision making. The most powerful tool that most production development decisions rely on is reservoir simulation with applications in multiple modeling procedures, such as individual simulation runs, history matching and production forecast and optimization. However, all of these applications lead to considerable computational time and computer resource-associated costs, rendering reservoir simulators as not fast and robust enough, and thus introducing the need for more time-efficient and intelligent tools, such as ML models which are able to adapt and provide fast and competent results that mimic the simulator’s performance within an acceptable error margin. In a recent paper, the developed ML applications in a subsurface reservoir simulation were reviewed, focusing on improving the speed and accuracy of individual reservoir simulation runs and history matching. This paper consists of the second part of that study, offering a detailed review of ML-based Production Forecast Optimization (PFO). This review can assist engineers as a complete source for applied ML techniques in reservoir simulation since, with the generation of large-scale data in everyday activities, ML is becoming a necessity for future and more efficient applications.

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Section: Dynamic Machine Learning Modelsmentioning

confidence: 99%

Section: Supervisedmentioning

confidence: 99%

Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II

Samnioti,

Gaganis

2023

Energies

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“…Chang et al apply the machine learning in transient surveillance in a deep-water oil field [24]. Pan et al utilize a physics-based approach to denoise the outliers and generate missing production history for a Cascated long short-term memory network for unconventional reservoirs [25]. These methods can handle huge amounts of dynamic data and have achieved certain success in field applications.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Surveillance Data in a Low-Permeability Carbonate Reservoir with the Machine-Learning Tree Boosting Method and the Time-Segmented Feature Extraction

Zhao

Xiao

2020

Energies

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Predictive analysis of the reservoir surveillance data is crucial for the high-efficiency management of oil and gas reservoirs. Here we introduce a new approach to reservoir surveillance that uses the machine learning tree boosting method to forecast production data. In this method, the prediction target is the decline rate of oil production at a given time for one well in the low-permeability carbonate reservoir. The input data to train the model includes reservoir production data (e.g., oil rate, water cut, gas oil ratio (GOR)) and reservoir operation data (e.g., history of choke size and shut-down activity) of 91 producers in this reservoir for the last 20 years. The tree boosting algorithm aims to quantitatively uncover the complicated hidden patterns between the target prediction parameter and other monitored data of a high variety, through state-of-the-art automatic classification and multiple linear regression algorithms. We also introduce a segmentation technique that divides the multivariate time-series production and operation data into a sequence of discrete segments. This feature extraction technique can transfer key features, based on expert knowledge derived from the in-reservoir surveillance, into a data form that is suitable for the machine learning algorithm. Compared with traditional methods, the approach proposed in this article can handle surveillance data in a multivariate time-series form with different strengths of internal correlation. It also provides capabilities for data obtained in multiple wells, measured from multiple sources, as well as of multiple attributes. Our application results indicate that this approach is quite promising in capturing the complicated patterns between the target variable and several other explanatory variables, and thus in predicting the daily oil production rate.

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Quantifying the effects of pressure management for the Williston basin Brine Extraction and Storage Test (BEST) site using machine learning

Yu,

Jiang,

Williamson

et al. 2024

International Journal of Greenhouse Gas Control

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An Effective Physics-Based Deep Learning Model for Enhancing Production Surveillance and Analysis in Unconventional Reservoirs

Cited by 16 publications

References 0 publications

Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II

Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II

Predicting the Surveillance Data in a Low-Permeability Carbonate Reservoir with the Machine-Learning Tree Boosting Method and the Time-Segmented Feature Extraction

Quantifying the effects of pressure management for the Williston basin Brine Extraction and Storage Test (BEST) site using machine learning

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