Optimization of tight gas reservoir fracturing parameters via gradient boosting regression modeling

Yang, Huohai; Liu, Xuanyu; Chu, Xiangshu; Xie, Binghong; Zhu, Ge; Li, Hancheng; Yang, Jun

doi:10.1016/j.heliyon.2024.e27015

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…The use of AI has been widely utilized across various fields, such as science [10]- [12], energy [13]- [15], and economics [16]- [18]. Some examples include ML techniques [19]- [21], ensemble techniques [22], [23], [22], [23], soft computing techniques [24], [25], statistical techniques [26], and fuzzy-based systems [27]. The effective application of AI in several O&G domains, such as gas [28], pipeline [29], crude oil [30], oxyhydrogen gas retrofit [31], and transformer oil [32], have increased interest in the last few years.…”

Section: Introductionmentioning

confidence: 99%

Predictive Analytics Performance on Oil and Gas: A Significant Review

R Azmi,

Yusoff,

Mohd Salledud-din

2024

Preprint

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Enhancing the management and monitoring of oil and gas processes demands developing precise predictive analytics techniques. Over the past two years, oil and its prediction have advanced significantly using conventional and modern Machine Learning techniques. Several review articles detail the developments in predictive maintenance and technical and non-technical aspects of influencing the uptake of big data. The absence of references for machine learning techniques impacts the effective optimization of predictive analytics in the oil and gas sector. This review paper offers readers thorough information on the latest machine learning methods utilized in this industry's predictive analytical modelling. The review covers forms of Machine Learning techniques used in predictive analytic modelling from 2021 to 2023 (91 articles). It provides an overview of the details of the papers that were reviewed, comprising of the model’s categories, the data's temporality, field, and name, the dataset's type, predictive analytics (classification or clustering or prediction), the models' input and output parameters, performance metrics, optimal model, and benefits and its drawbacks. Additionally, suggestions for future research directions are provided to raise the potential of the associated knowledge and increase the accuracy of oil and gas predictive analytics models.

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Section: Introductionmentioning

confidence: 99%

Predictive Analytics Performance on Oil and Gas: A Significant Review

R Azmi,

Yusoff,

Mohd Salledud-din

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The use of AI has been widely utilized in various fields, such as science [ 13 , 14 , 15 ], energy [ 16 , 17 , 18 ], and economics [ 19 , 20 , 21 ]. Some examples include ML techniques [ 22 , 23 , 24 ], ensemble techniques [ 25 , 26 ], soft computing techniques [ 27 , 28 ], statistical techniques [ 29 ], and fuzzy-based systems [ 30 ]. The effective application of AI in several O&G domains, such as gas [ 31 ], pipeline [ 32 ], crude oil [ 33 ], oxyhydrogen gas retrofit [ 34 ], and transformer oil [ 35 ], has received increased interest in the last few years.…”

Section: Introductionmentioning

confidence: 99%

A Review of Predictive Analytics Models in the Oil and Gas Industries

R Azmi,

Yusoff,

Mohd Sallehud-din

2024

Sensors

View full text Add to dashboard Cite

Enhancing the management and monitoring of oil and gas processes demands the development of precise predictive analytic techniques. Over the past two years, oil and its prediction have advanced significantly using conventional and modern machine learning techniques. Several review articles detail the developments in predictive maintenance and the technical and non-technical aspects of influencing the uptake of big data. The absence of references for machine learning techniques impacts the effective optimization of predictive analytics in the oil and gas sectors. This review paper offers readers thorough information on the latest machine learning methods utilized in this industry’s predictive analytical modeling. This review covers different forms of machine learning techniques used in predictive analytical modeling from 2021 to 2023 (91 articles). It provides an overview of the details of the papers that were reviewed, describing the model’s categories, the data’s temporality, field, and name, the dataset’s type, predictive analytics (classification, clustering, or prediction), the models’ input and output parameters, the performance metrics, the optimal model, and the model’s benefits and drawbacks. In addition, suggestions for future research directions to provide insights into the potential applications of the associated knowledge. This review can serve as a guide to enhance the effectiveness of predictive analytics models in the oil and gas industries.

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Optimization of Fine-Fracture Distribution Patterns for Multi-Stage and Multi-Cluster Fractured Horizontal Wells in Tight Gas Reservoirs

Ren,

Wang,

Zhao

et al. 2024

Processes

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The efficient development of tight gas reservoirs is significantly enhanced by multi-stage and multi-cluster fracturing techniques in conjunction with horizontal well technology, leading to substantial increases in reservoir drainage volume and individual well productivity. This study presents a tailored fine-fracturing approach for horizontal wells in tight gas reservoirs, supported by a gas–water two-phase numerical simulation model. Utilizing the orthogonal experimental design method, we simulated and optimized various fracture distribution schemes to refine fracturing parameters for maximum efficiency. The optimization was further validated through a comparison with actual well completion and development dynamics. The quantitative results highlight the optimal fracture distribution for horizontal wells, with a horizontal section length of 1400 to 1600 m and 14 to 16 fracturing stages. The pattern features a “dense at both ends and sparse in the middle” strategy, with stage spacing of 80 to 110 m, and a “longer in the middle and shorter at both ends” fracture half-length of 100 to 140 m, achieving a fracture conductivity of 30 μm2·cm. To ensure the economic feasibility of the proposed fracturing strategy, we conducted an economic evaluation using the net present value (NPV) method, which confirmed the robustness of the optimization outcomes in terms of both technical performance and economic viability. The reliability of these optimization outcomes has been confirmed through practical application in the development of horizontal wells in the study area. This research approach and methodology can provide theoretical guidance for the design of hydraulic fracturing operations and the integration of geological and engineering practices in similar unconventional oil and gas reservoirs.

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Optimization of tight gas reservoir fracturing parameters via gradient boosting regression modeling

Cited by 3 publications

References 19 publications

Predictive Analytics Performance on Oil and Gas: A Significant Review

Predictive Analytics Performance on Oil and Gas: A Significant Review

A Review of Predictive Analytics Models in the Oil and Gas Industries

Optimization of Fine-Fracture Distribution Patterns for Multi-Stage and Multi-Cluster Fractured Horizontal Wells in Tight Gas Reservoirs

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