Data-Driven Models to Predict Hydrocarbon Production From Unconventional Reservoirs by Thermal Recovery

Lee, Kyung Jae

doi:10.1115/1.4047309

Cited by 13 publications

(1 citation statement)

References 44 publications

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“…Shunde Yin performed a sensitivity analysis on the effect of fracturing parameters such as fluid rate, injection fluid temperature, aquifer stiffness and permeability on fracture propagation patterns during fracturing using an extended finite element method (Yin, 2013; Yin, 2018). Many data-driven forecasting models have been applied in unconventional oil and gas with positive results (Kyung-Jae, 2020). Westwood conducted a sensitivity analysis via the Monte Carlo method to investigate the effect of pumping time and injection pressure differential on fracture area and the lateral distance at which hydraulic fracturing should occur in order not to reactivate the fault (Westwood et al ., 2017).…”

Section: Introductionmentioning

confidence: 99%

Multivariate nonlinear regression analysis of hydraulic fracturing parameters based on hybrid FEM–DEM

Li,

Lan

2023

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PurposeThis paper aims to employ a multivariate nonlinear regression analysis to establish a predictive model for the final fracture area, while accounting for the impact of individual parameters.Design/methodology/approachThis analysis is based on the numerical simulation data obtained, using the hybrid finite element–discrete element (FE–DE) method. The forecasting model was compared with the numerical results and the accuracy of the model was evaluated by the root mean square (RMS) and the RMS error, the mean absolute error and the mean absolute percentage error.FindingsThe multivariate nonlinear regression model can accurately predict the nonlinear relationships between injection rate, leakoff coefficient, elastic modulus, permeability, Poisson’s ratio, pore pressure and final fracture area. The regression equations obtained from the Newton iteration of the least squares method are strong in terms of the fit to the six sensitive parameters, and the model follow essentially the same trend with the numerical simulation data, with no systematic divergence detected. Least absolutely deviation has a significantly weaker performance than the least squares method. The percentage contribution of sensitive parameters to the final fracture area is available from the simulation results and forecast model. Injection rate, leakoff coefficient, permeability, elastic modulus, pore pressure and Poisson’s ratio contribute 43.4%, −19.4%, 24.8%, −19.2%, −21.3% and 10.1% to the final fracture area, respectively, as they increased gradually. In summary, (1) the fluid injection rate has the greatest influence on the final fracture area. (2)The multivariate nonlinear regression equation was optimally obtained after 59 iterations of the least squares-based Newton method and 27 derivative evaluations, with a decidability coefficient R2 = 0.711 representing the model reliability and the regression equations fit the four parameters of leakoff coefficient, permeability, elastic modulus and pore pressure very satisfactorily. The models follow essentially the identical trend with the numerical simulation data and there is no systematic divergence. The least absolute deviation has a significantly weaker fit than the least squares method. (3)The nonlinear forecasting model of physical parameters of hydraulic fracturing established in this paper can be applied as a standard for optimizing the fracturing strategy and predicting the fracturing efficiency in situ field and numerical simulation. Its effectiveness can be trained and optimized by experimental and simulation data, and taking into account more basic data and establishing regression equations, containing more fracturing parameters will be the further research interests.Originality/valueThe nonlinear forecasting model of physical parameters of hydraulic fracturing established in this paper can be applied as a standard for optimizing the fracturing strategy and predicting the fracturing efficiency in situ field and numerical simulation. Its effectiveness can be trained and optimized by experimental and simulation data, and taking into account more basic data and establishing regression equations, containing more fracturing parameters will be the further research interests.

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Section: Introductionmentioning

confidence: 99%

Multivariate nonlinear regression analysis of hydraulic fracturing parameters based on hybrid FEM–DEM

Li,

Lan

2023

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A Comprehensive review of data-driven approaches for forecasting production from unconventional reservoirs: best practices and future directions

Rahmanifard,

Gates

2024

Artif Intell Rev

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Prediction of well production from unconventional reservoirs is a complex problem given an incomplete understanding of physics despite large amounts of data. Recently, Data Analytics Techniques (DAT) have emerged as an effective approach for production forecasting for unconventional reservoirs. In some of these approaches, DAT are combined with physics-based models to capture the essential physical mechanisms of fluid flow in porous media, while leveraging the power of data-driven methods to account for uncertainties and heterogeneities. Here, we provide an overview of the applications and performance of DAT for production forecasting of unconventional reservoirs examining and comparing predictive models using different algorithms, validation benchmarks, input data, number of wells, and formation types. We also discuss the strengths and limitations of each model, as well as the challenges and opportunities for future research in this field. Our analysis shows that machine learning (ML) based models can achieve satisfactory performance in forecasting production from unconventional reservoirs. We measure the performance of the models using two dimensionless metrics: mean absolute percentage error (MAPE) and coefficient of determination (R2). The predicted and actual production data show a high degree of agreement, as most of the models have a low error rate and a strong correlation. Specifically, ~ 65% of the models have MAPE less than 20%, and more than 80% of the models have R2 higher than 0.6. Therefore, we expect that DAT can improve the reliability and robustness of production forecasting for unconventional resources. However, we also identify some areas for future improvement, such as developing new ML algorithms, combining DAT with physics-based models, and establishing multi-perspective approaches for comparing model performance.

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Predicting CO2 trapping in deep saline aquifers using optimized long short-term memory

Al-qaness

Ewees

Thanh

et al. 2022

Environ Sci Pollut Res

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Data-Driven Models to Predict Hydrocarbon Production From Unconventional Reservoirs by Thermal Recovery

Cited by 13 publications

References 44 publications

Multivariate nonlinear regression analysis of hydraulic fracturing parameters based on hybrid FEM–DEM

Multivariate nonlinear regression analysis of hydraulic fracturing parameters based on hybrid FEM–DEM

A Comprehensive review of data-driven approaches for forecasting production from unconventional reservoirs: best practices and future directions

Predicting CO2 trapping in deep saline aquifers using optimized long short-term memory

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