Method and practice of deep favorable shale reservoirs prediction based on machine learning

Bing-jie, Cheng; Xu, Tianji; LUO, Shiyi; CHEN, Tianjie; LI, Yongsheng; Jianming, Tang

doi:10.1016/s1876-3804(22)60332-x

Cited by 9 publications

(7 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After that, many scholars will predict the production of oil and gas wells based on neural networks. The representative scholars are Liu Wei [25] (2020), Gu Jianwei [26] (2020), Ma Xianlin [27] (2021), Han Shan [28] (2022), Cheng Bingjie [29] (2022), and Li Juhua [30] (2023). Although the above scholars have applied a big data approach to the field of tight well production prediction, most of the models are purely data-driven.…”

Section: Introductionmentioning

confidence: 99%

Production Prediction Model of Tight Gas Well Based on Neural Network Driven by Decline Curve and Data

Chen,

Qu,

Liu

et al. 2024

Processes

View full text Add to dashboard Cite

The accurate prediction of gas well production is one of the key factors affecting the economical and efficient development of tight gas wells. The traditional oil and gas well production prediction method assumes strict conditions and has a low prediction accuracy in actual field applications. At present, intelligent algorithms based on big data have been applied in oil and gas well production prediction, but there are still some limitations. Only learning from data leads to the poor generalization ability and anti-interference ability of prediction models. To solve this problem, a production prediction method of tight gas wells based on the decline curve and data-driven neural network is established in this paper. Based on the actual production data of fractured horizontal wells in three tight gas reservoirs in the Ordos Basin, the prediction effect of the Arps decline curve model, the SPED decline curve model, the MFF decline curve model, and the combination of the decline curve and data-driven neural network model is compared and analyzed. The results of the case analysis show that the MFF model and the combined data-driven model have the highest accuracy, the average absolute percentage error is 14.11%, and the root-mean-square error is 1.491, which provides a new method for the production prediction of tight gas wells in the Ordos Basin.

show abstract

Section: Introductionmentioning

confidence: 99%

Production Prediction Model of Tight Gas Well Based on Neural Network Driven by Decline Curve and Data

Chen,

Qu,

Liu

et al. 2024

Processes

View full text Add to dashboard Cite

show abstract

“…This extrapolation leans heavily on historical production metrics and pertinent reservoir data [8][9][10]. Another strategy is based on the production potentialities of adjacent wells [11,12], representing the extant geological and geophysical data on the target reservoir. This approach evaluates the economic viability of implementing new drillings and finding the sweet spots within the reservoir's geological and engineering landscape [13,14].…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning has recently gained popularity in production prediction [11,12]. The estimated ultimate recovery (EUR) prediction model for shale gas wells is based on the multiple linear regression method.…”

Section: Introductionmentioning

confidence: 99%

Post-Fracture Production Prediction with Production Segmentation and Well Logging: Harnessing Pipelines and Hyperparameter Tuning with GridSearchCV

Sun,

Wang,

Wang

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

As the petroleum industry increasingly exploits unconventional reservoirs with low permeability and porosity, accurate predictions of post-fracture production are becoming critical for investment decisions, energy policy development, and environmental impact assessments. However, despite extensive research, accurately forecasting post-fracture production using well-log data continues to be a complex challenge. This study introduces a new method of data volume expansion, which is to subdivide the gas production of each well on the first day according to the depth of logging data, and to rely on the correlation model between petrophysical parameters and gas production to accurately combine the gas production data while matching the accuracy of the well-log data. Twelve pipelines were constructed utilizing a range of techniques to fit the regression relationship between logging parameters and post-fracture gas production These included data preprocessing methods (StandardScaler and RobustScaler), feature extraction approaches (PCA and PolynomialFeatures), and advanced machine learning models (XGBoost, Random Forest, and neural networks). Hyperparameter optimization was executed via GridSearchCV. To assess the efficacy of diverse models, metrics including the coefficient of determination (R2), standard deviation (SD), Pearson correlation coefficient (PCC), mean absolute error (MAE), mean squared error (MSE), and root-mean-square error (RMSE) were invoked. Among the several pipelines explored, the PFS-NN exhibited excellent predictive capability in specific reservoir contexts. In essence, integrating machine learning with logging parameters can be used to effectively assess reservoir productivity at multi-meter formation scales. This strategy not only mitigates uncertainties endemic to reservoir exploration but also equips petroleum engineers with the ability to monitor reservoir dynamics, thereby facilitating reservoir development. Additionally, this approach provides reservoir engineers with an efficient means of reservoir performance oversight.

show abstract

“…The gas content logging prediction method includes two approaches. One method involves using a machine learning algorithm to fit the measured gas content in the field. , Another approach is to calculate water saturation by utilizing logging theoretical models such as Simandoux, modified Simandoux, total shale, modified total shale, Indonesian, dual-water, and dispersed clay, − and subsequently derive the gas content. For low-resistivity shale gas reservoirs, organic matter and pyrite can be incorporated into the aforementioned model to enhance the calculation accuracy. , The drawback of these two methods is that the machine learning algorithm performs better in training wells or known low-resistivity shale gas reservoirs, and when the trained model is applied to other wells, accurate predictions may not be achieved.…”

Section: Introductionmentioning

confidence: 99%

Genesis, Identification Method, and Exploration Potential Evaluation of Marine Low-Resistivity Shale Gas Reservoirs

Wu,

Jiang,

Cao

et al. 2024

Energy Fuels

View full text Add to dashboard Cite

Method and practice of deep favorable shale reservoirs prediction based on machine learning

Cited by 9 publications

References 13 publications

Production Prediction Model of Tight Gas Well Based on Neural Network Driven by Decline Curve and Data

Production Prediction Model of Tight Gas Well Based on Neural Network Driven by Decline Curve and Data

Post-Fracture Production Prediction with Production Segmentation and Well Logging: Harnessing Pipelines and Hyperparameter Tuning with GridSearchCV

Genesis, Identification Method, and Exploration Potential Evaluation of Marine Low-Resistivity Shale Gas Reservoirs

Contact Info

Product

Resources

About