Long-term production forecast in tight and shale reservoirs: Adapting probability density functions for decline curve analysis

Alimohammadi, Hamzeh; Chen, Shengnan Nancy

doi:10.1016/j.jgsce.2023.205113

Cited by 5 publications

(1 citation statement)

References 17 publications

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“…Machine learning (ML) excels in extracting information from high-dimensional, complex data, playing a significant role in predicting reservoir production capacity automatically in supervised or unsupervised modes [24]. Extensive studies by scholars have shown the superiority of ML algorithms over traditional prediction methods like logistic regression in forecasting reservoir production capacity [25,26]. Zhou et al [27] combined principal component analysis, cluster analysis, and regression analysis to compute shale gas production in specific regions based on variables such as hydraulic fracture count, well deviation, vertical depth, and fracturing fluid.…”

Section: Corresponding Loss Function Y Leftmentioning

confidence: 99%

Production Capacity Prediction Method of Shale Oil Based on Machine Learning Combination Model

Qian,

Lu,

Zhong

et al. 2024

ENERGY

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The production capacity of shale oil reservoirs after hydraulic fracturing is influenced by a complex interplay involving geological characteristics, engineering quality, and well conditions. These relationships, nonlinear in nature, pose challenges for accurate description through physical models. While field data provides insights into real-world effects, its limited volume and quality restrict its utility. Complementing this, numerical simulation models offer effective support. To harness the strengths of both data-driven and model-driven approaches, this study established a shale oil production capacity prediction model based on a machine learning combination model. Leveraging fracturing development data from 236 wells in the field, a data-driven method employing the random forest algorithm is implemented to identify the main controlling factors for different types of shale oil reservoirs. Through the combination model integrating support vector machine (SVM) algorithm and back propagation neural network (BPNN), a model-driven shale oil production capacity prediction model is developed, capable of swiftly responding to shale oil development performance under varying geological, fluid, and well conditions. The results of numerical experiments show that the proposed method demonstrates a notable enhancement in R 2 by 22.5% and 5.8% compared to singular machine learning models like SVM and BPNN, showcasing its superior precision in predicting shale oil production capacity across diverse datasets.

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Section: Corresponding Loss Function Y Leftmentioning

confidence: 99%

Production Capacity Prediction Method of Shale Oil Based on Machine Learning Combination Model

Qian,

Lu,

Zhong

et al. 2024

ENERGY

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A novel EUR prediction model for fractured horizontal shale gas wells based on material balance theory

Liang,

You,

et al. 2024

Natural Gas Industry B

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A Hybrid Tabular-Spatial-Temporal Model with 3D Geo-Model for Production Prediction in Shale Gas Formations

Wang,

Chen

et al. 2024

SPE Annual Technical Conference and Exhibition

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The evolution of shale gas production has reshaped North America's energy profile. Utilizing the vast amounts of data generated from production and operations, machine learning offers significant advantages in production forecasting and performance optimization. This study proposed a pioneering hybrid model integrating tabular, spatial, and temporal modalities to enhance production forecasting in unconventional shale gas reservoirs. Despite traditional methods such as artificial neural networks (ANN) and XGBoost, which rely solely on tabular data for training and prediction, this study proposes a novel 3D-parameterization method. This approach tokenizes the formation property distribution into 3-axis tensors, enabling a more comprehensive representation of spatial data. Then, a 3D-convolutional neural network (3D-CNN) with the attention mechanism module was established to process the created spatial data. For temporal modality, the long short-term memory (LSTM) module was used to accept the dynamic input and predict the monthly production simultaneously. A total of 677 wells data from Duvernay formation was collected, pre-processed and fed into the according module based on their modality. The results show that the model combined three modalities achieved an impressive level of accuracy, with a coefficient of determination (R2) of 0.8771, surpassing the tabular (0.7841) and tabular-spatial (0.8230) modalities models. Additionally, global optimization was applied to further enhance the model performance by optimizing the architecture of each module and model hyperparameters, and a 1.88% improvement was achieved from the empirical design. These advancements set a new benchmark for predictive modelling in unconventional shale gas reservoirs, highlighting the importance of utilizing data from different modalities in improving production forecast prediction.

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Long-term production forecast in tight and shale reservoirs: Adapting probability density functions for decline curve analysis

Cited by 5 publications

References 17 publications

Production Capacity Prediction Method of Shale Oil Based on Machine Learning Combination Model

Production Capacity Prediction Method of Shale Oil Based on Machine Learning Combination Model

A novel EUR prediction model for fractured horizontal shale gas wells based on material balance theory

A Hybrid Tabular-Spatial-Temporal Model with 3D Geo-Model for Production Prediction in Shale Gas Formations

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