Data-Driven Productivity Regularity Research of Fractured Well in Tight Gas Reservoir

Zhang, Qiang; Yao, Weiying; Xia, Huan-yu; Ma, Wendong; Liu, Cheng-zhen

doi:10.1007/978-981-19-2149-0_118

Cited by 1 publication

(1 citation statement)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, scholars have conducted a series of studies using machine learning methods for single-well production prediction in unconventional gas reservoirs. In 2015, Ma established a sample set of influencing factors on production capacity [17]. They used BP neural network and support vector machine methods to predict the production rate of tight gas wells.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Single-Well Production Rate after Hydraulic Fracturing in Unconventional Gas Reservoirs Based on Ensemble Learning Model

Ye,

Li,

Zhang

et al. 2024

Processes

View full text Add to dashboard Cite

To address the significant challenges in determining the single-well production of tight gas and shale gas after hydraulic fracturing, artificial intelligence (AI) methods were implemented. Machine learning (ML) algorithms such as random forest (RF), extremely randomized trees (ET), lightweight gradient boosting machines (LightGBM), gradient boosting regression (GBR), and linear regression (LR) were utilized in conjunction with reservoir geology, engineering parameters, and production data to develop several foundational models for forecasting the production of unconventional gas wells. The accuracy of these models was evaluated. Based on this, improvements in the models’ predictive accuracy and generalizability were achieved through the ensemble of machine learning models. Furthermore, this paper selected two representative tight and shale gas reservoirs to demonstrate the application of the ensemble model for well production forecasting, and a comparative analysis with actual production data was conducted. For tight gas reservoir A, the blending model achieved an MAE of 0.8419 and an MSE of 1.0930, with an R2 score of 0.8812. For shale gas reservoir B, the blending model achieved an MAE of 1.4841 and an MSE of 3.1629, with an R2 score of 0.9524. The results of the case studies indicate that the ensemble model approach employed in this study has a higher predictive accuracy and reliability than a single machine learning algorithm, and is capable of handling high-dimensional, large-scale, and imbalanced data, offering scientific validation and technical support for the assessment of the well productivity in tight and shale gas wells.

show abstract

Section: Introductionmentioning

confidence: 99%