Development of shale gas production prediction models based on machine learning using early data

Niu, Wente; Lu, Jialiang; Sun, Yize

doi:10.1016/j.egyr.2021.12.040

Cited by 40 publications

(16 citation statements)

References 33 publications

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“…Excellent prediction of gas production can be achieving using AI-based methods with input of actual shale production data and geological and engineering design parameters. 129 AI has obvious advantages over traditional methods of shale gas research. For example, it is costly to conduct repeated tests for the complex multiphase properties and flow process of shale gas, and the research cycle is long.…”

Section: Application Of Ai Methods In Complex Physicalmentioning

confidence: 99%

“…Due to the advantage of few hypotheses in physical mechanism, AI-based EUR prediction models have been established. − The most common ML-based models for prediction of gas production performance include decision tree model, random forest regression, gradient boosted machine, support vector machine, etc. Excellent prediction of gas production can be achieving using AI-based methods with input of actual shale production data and geological and engineering design parameters . AI has obvious advantages over traditional methods of shale gas research.…”

Section: Shale Structure Characterization and Gas Transport Predictio...mentioning

confidence: 99%

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Review of Shale Gas Transport Prediction: Basic Theory, Numerical Simulation, Application of AI Methods, and Perspectives

et al. 2023

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The gas transport mechanism in shale reservoirs is extremely complex and is a typical multiscale and multiphysics coupled transport process, considering the complex shale rock structure, wide distribution of micropores and nanopores in shale gas reservoirs, diverse gas occurrence forms, and large pore size spans. An accurate understanding of the shale gas transport process and mechanism is important for effective exploration of shale gas reservoirs. In this work, a review of the recent progress in the prediction of shale gas transport in porous media is presented. The basic theory of gas transport in nanopores is discussed. The gas transport in organic and inorganic matter and the gas adsorption effect are covered. Then, gas transport simulations with conventional multiscale numerical methods, including molecular dynamics and lattice Boltzmann simulations, are reviewed, and the multiscale modeling methods are discussed. Furthermore, the application of artificial intelligence (AI) methods in shale gas transport research is discussed. The focus is on the characterization of the shale porous geometry, including porosity, tortuosity, pore size distribution, and reconstruction of the shale porous medium. The application of AI-based methods such as neural networks and machine learning for the prediction of porous flow properties is discussed. This study intends to provide a comprehensive review of shale gas transport characteristics and to enable the accessibility of AI tools in the research of shale gas.

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Section: Application Of Ai Methods In Complex Physicalmentioning

confidence: 99%

Section: Shale Structure Characterization and Gas Transport Predictio...mentioning

confidence: 99%

Review of Shale Gas Transport Prediction: Basic Theory, Numerical Simulation, Application of AI Methods, and Perspectives

et al. 2023

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“…However, due to the many idealized assumptions in the analytical model, the analytical method cannot accurately reflect the complex flow mechanisms and development laws of shale gas in the actual formation [56][57][58]. With the continuous integration of the artificial intelligence with oil and gas field production sites, machine learning methods are gradually emphasized in the productivity evaluation of shale gas wells [59][60][61][62].…”

Section: Introductionmentioning

confidence: 99%

Research on Productivity Evaluation Method Based on Shale Gas Well Early Flow Stage Data

Huang

Guo

et al. 2023

Geofluids

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Since the accurate early gas well production regime is related to the production period and final productivity, it is crucial to make better use of early flow stage production data for gas well productivity evaluation. Absolute open flow potential (AOF) and estimated ultimate recovery (EUR) are essential parameters for evaluating the productivity of shale gas wells. This study establishes new AOF calculation methods for the early production stage. The analytical model can calculate the AOF only by using stable pressure and production data in the flowback stage, which greatly improves the efficiency of productivity evaluation. Three methods have, respectively, calculated the productivity of the shale gas wells above 3500 m in the Luzhou block. The results show that well L2-3 has the highest AOF, averaging 278.1 × 10 4 m 3 / d , whereas well Y2-8 has the lowest AOF, averaging 100.2 × 10 4 m 3 / d . Different AOF calculation methods are identified for gas wells in different stages of production. For gas wells in the initial unstable flow stage, a pseudogas production index method is recommended. A water production index analysis method, with lower evaluation results, is proposed for gas wells in the flowback stage. A modern production decline analysis method is found to be preferred for calculating the EUR of deep shale gas wells. Well L2-3 has the highest average EUR of 1.26 × 10 8 m 3 , whereas well Y2-8 has the lowest average EUR of 0.42 × 10 8 m 3 . The Blasingame method is recommended for medium-to-high-production gas wells, whereas a normalized pressure integral method is suggested for low-production wells. A strong exponential quantitative link between the AOF and the EUR shows that a fracture system’s initial productivity has a significant impact on a well’s EUR. The findings of this study enrich the productivity evaluation system, increase the accuracy of productivity evaluation results, and provide theoretical support for deep shale gas wells.

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“…Yang et al developed a short-term memory model based on deep learning to predict shale gas production and compared it with conventional analytical methods. Moreover, different machine learning techniques have been used to predict the estimated ultimate recovery (EUR) of shale gas. − Meanwhile, for flowback water rate, a paper has been presented that implements MLP-based deep learning to forecast the flowback of shale gas wells using characteristic flowback factors and data from 286 shale gas wells in the Weiyuan field, China . It should be noticed that in the aforementioned studies, the development of machine learning models for forecasting gas production and flowback water have been treated individually.…”

Section: Introductionmentioning

confidence: 99%

Development and Evaluation of Deep Learning Models for Forecasting Gas Production and Flowback Water in Shale Gas Reservoirs

López-Flores

Lira-Barragán

Rubio-Castro

et al. 2023

Ind. Eng. Chem. Res.

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In this paper, deep learning models are developed based on a multilayer perceptron to forecast 12 month cumulative produced shale gas and 90 day produced flowback water using a study area within the Eagle Ford Formation as a database. These models can help decision-makers have important references when drilling new wells. Latitude, longitude, true vertical depth, lateral longitude, total proppant, and total fracture water are used as input variables. The trained models are evaluated in a study area within the Burgos Basin to analyze the energy benefits, freshwater consumption, and flowback water produced through well drilling for the first time in Mexico. A non-dominated sorting genetic algorithm is used for the minimization of total freshwater consumption and maximization of cumulative produced gas. The multilayer perceptron models showed good results. The coefficient of determination and the mean absolute error of the single-output models remained above 0.93 and below 3.00 for the validation set, respectively.

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Development of shale gas production prediction models based on machine learning using early data

Cited by 40 publications

References 33 publications

Review of Shale Gas Transport Prediction: Basic Theory, Numerical Simulation, Application of AI Methods, and Perspectives

Review of Shale Gas Transport Prediction: Basic Theory, Numerical Simulation, Application of AI Methods, and Perspectives

Research on Productivity Evaluation Method Based on Shale Gas Well Early Flow Stage Data

Development and Evaluation of Deep Learning Models for Forecasting Gas Production and Flowback Water in Shale Gas Reservoirs

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