Practical Aspects of Hydraulic Fracturing Design Optimization using Machine Learning on Field Data: Digital Database, Algorithms and Planning the Field Tests (Russian)

Duplyakov, Viktor; Morozov, Aleksandr; Popkov, Dmitry; Vainshtein, Albert; Осипцов, А. Н.; Burnaev, Evgeny; Shel, Egor; Paderin, Grigory; Kabanova, Polina; Fayzullin, Ildar; Uchuev, Ruslan; Mukhametov, Albert; Prutsakov, Alexander Sergeevich; Vikhman, Ivan; Staritsyn, Maxim

doi:10.2118/203890-ru

Cited by 15 publications

(6 citation statements)

References 7 publications

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“…However, few of them elucidate a process of data preprocessing tailored to specific situations. Researchers predominantly concentrate on aspects like the dataset's source and the division of training and validation sets when discussing data preprocessing [20,50,55], yet there is a noticeable absence of guidance on handling missing data, data cleansing, annotation, and similar procedures. Data preprocessing for specific situations is often crucial for the success of petroleum engineering.…”

Section: The Pros and Cons Of Machine Learning Methods And The Possib...mentioning

confidence: 99%

“…Optimization of production enhancement EC (evolutionary algorithm) [20] 2020; ML-PSO [21] 2024 ensemble learning [22] 2019; ANN [23] 2019…”

Section: Optimization Of Formation Stimulationmentioning

confidence: 99%

“…Taking production enhancement as the optimization direction, a data-driven approach is adopted, relying on machine learning methods to mine the degree of influence of each parameter on production enhancement, then optimize the design. Duplyakov V. et al [20] introduced evolutionary algorithms and data mining techniques to learn from formation, well, and fracking process parameters containing about 5500 points to build a model for the prediction of cumulative production, which was used for further optimization of hydraulic fracturing design. The reservoir in the Kuche foreland of the Tarim Basin in China is an ultra-deep HTHP (high-temperature, high-pressure) naturally fractured sandstone reservoir.…”

Section: Design Optimizationmentioning

confidence: 99%

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Machine Learning in Reservoir Engineering: A Review

Zhou,

Liu,

Liu

et al. 2024

Processes

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With the rapid progress of big data and artificial intelligence, machine learning technologies such as learning and adaptive control have emerged as a research focus in petroleum engineering. They have various applications in oilfield development, such as parameter prediction, optimization scheme deployment, and performance evaluation. This paper provides a comprehensive review of these applications in three key scenarios of petroleum engineering, namely hydraulic fracturing and acidizing, chemical flooding and gas flooding, and water injection. This article first introduces the steps and methods of machine learning processing in these scenarios, then discusses the advantages, disadvantages, existing challenges, and future prospects of these machine learning methods. Furthermore, this article compares and contrasts the strengths and weaknesses of these machine learning methods, aiming to help researchers select and improve their methods. Finally, this paper identifies some potential development trends and research directions of machine learning in petroleum engineering based on the current issues.

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Section: The Pros and Cons Of Machine Learning Methods And The Possib...mentioning

confidence: 99%

“…Optimization of production enhancement EC (evolutionary algorithm) [20] 2020; ML-PSO [21] 2024 ensemble learning [22] 2019; ANN [23] 2019…”

Section: Optimization Of Formation Stimulationmentioning

confidence: 99%

Section: Design Optimizationmentioning

confidence: 99%

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Machine Learning in Reservoir Engineering: A Review

Zhou,

Liu,

Liu

et al. 2024

Processes

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“…The outcomes demonstrated that machine learning models accurately forecasted well production performance and offered recommendations for production optimization. Duplyakov et al (2020) employed machine learning techniques for optimizing hydraulic fracturing design using field data. They developed a digital database and employed various machine learning algorithms to analyze and model field data, predicting optimal fracturing design parameters and production enhancement.…”

Section: Introductionmentioning

confidence: 99%

An Innovative Method for Comprehensive Optimization of Hydraulic Fracturing Parameters to Enhance Production in Tight Oil Reservoirs

2023

Improv Oil Gas Recover

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The hydraulic fracturing technology for horizontal wells is one of the key techniques for the effective development of tight oil and gas reservoirs. Optimizing fracturing parameters can significantly enhance fracturing effectiveness, reduce development risks, and improve oil and gas production as well as economic efficiency. Rapid and accurate optimization of hydraulic fracturing construction parameters for tight oil horizontal wells has always been a challenge in reservoir development and management. This study introduces a novel workflow for optimizing fracturing parameters by combining reservoir numerical simulation and machine learning techniques. The paper first establishes a single-well numerical model using commercial simulator, and calibrates the reservoir model through matching historical production data. Eight main parameters are selected, and an initial feature dataset is generated using Monte Carlo method, while production dataset is obtained from reservoir numerical simulation. Subsequently, various machine learning algorithms are employed to construct fracture productivity models under different combinations of reservoir and hydraulic fracture parameters. The selected machine learning model with best performance is then integrated with an economic evaluation model to establish an optimization model for hydraulic fracturing parameters optimization for tight oil horizontal wells. The research indicates that the production prediction model established based on the CNN-LSTM method exhibits a high level of accuracy. The optimization model for hydraulic fracturing parameters in tight oil horizontal wells can rapidly optimize fracturing parameters. The proposed methodology in the paper has the potential to enhance horizontal well production and improve economic benefits in tight oil horizontal wells, and can also be applied to similar field development and engineering parameter optimization scenarios.

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“…Table 1 summarizes some examples of current domestic and foreign research on fracturing parameter optimization [12,[14][15][16][17][18][19]. From a survey of the current domestic and foreign research status, it is found that machine learning methods related to fracturing parameter optimization are mainly focused on shale gas, with relatively few studies on shale oil, and even fewer studies on machine learning-based fracturing parameter optimization for Chinese shale oil reservoirs.…”

Section: Introductionmentioning

confidence: 99%

Explainable Machine Learning-Based Method for Fracturing Prediction of Horizontal Shale Oil Wells

Liu,

Zhang,

Yang

et al. 2023

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Hydraulic fracturing is a crucial method in shale oil development, and predicting production after hydraulic fracturing is one of the challenges in shale oil development. Conventional methods for predicting production include analytical methods and numerical simulation methods, but these methods involve many parameters, have high uncertainty, and are time-consuming and costly. With the development of shale oil development, there are more and more sample data on the geological parameters, engineering parameters, and development parameters of shale oil hydraulic fracturing, making it possible to use machine learning methods to predict production after hydraulic fracturing. This article first analyzes the impact of different parameters on initial production and recoverable reserves based on field data from Chang-7 shale oil in the Ordos Basin of China. Then, using the Particle Swarm Optimization (PSO) algorithm and the Gradient Boosting Decision Tree (GBDT) algorithm, machine learning models for initial production and recoverable reserves are established. The Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP) explanation methods are used to explain the models. The study found that initial production is highly correlated with parameters such as the number of fracturing stages and fracturing fluid volume, while recoverable reserves are significantly related to parameters such as well spacing, area, and reserver-controlled. The PSO-GBDT model established in this study has an accuracy of over 85% and can be used for production prediction and subsequent parameter optimization research. By comparing the LIME and SHAP local explanation methods, it is shown that different explanation methods can obtain reasonable and credible local explanation results. This article establishes a high-precision shale oil well production prediction model and two model interpretation methods, which could provide technical support for shale oil well production prediction and production analysis.

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Practical Aspects of Hydraulic Fracturing Design Optimization using Machine Learning on Field Data: Digital Database, Algorithms and Planning the Field Tests (Russian)

Cited by 15 publications

References 7 publications

Machine Learning in Reservoir Engineering: A Review

Machine Learning in Reservoir Engineering: A Review

An Innovative Method for Comprehensive Optimization of Hydraulic Fracturing Parameters to Enhance Production in Tight Oil Reservoirs

Explainable Machine Learning-Based Method for Fracturing Prediction of Horizontal Shale Oil Wells

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