A new rate-decline analysis of shale gas reservoirs: Coupling the self-diffusion and surface diffusion characteristics

Miao, Yuqing; Li, Xiangfang; Lee, John; Yu, Zhou; Wu, Keliu; Sun, Zheng; Liu, Songxia

doi:10.1016/j.petrol.2017.12.097

Cited by 32 publications

(5 citation statements)

References 47 publications

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“…A common method is the stretched exponential production decline (SEPD) (Valko 2009), in addition to the Arps hyperbolic decline with a "best-fit" hyperbolic decline exponent "b" value. But all are based on empirical observations of a particular scenario and have their own limitations, which can yield unreasonable EUR estimation (Ilk et al 2008;Mahmoud et al 2018;Miao et al 2018;Valko 2009;Zhang et al 2015;Alarifi 2021).…”

Section: Eur Estimation Methodsmentioning

confidence: 99%

RETRACTED ARTICLE: Data-driven EUR for multistage hydraulically fractured wells in shale formation using different machine learning methods

Ibrahim

Alarifi

Elkatatny

2023

J Petrol Explor Prod Technol

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This study proposes the use of different machine learning techniques to predict the estimated ultimate recovery (EUR) as a function of the hydraulic fracturing design. A set of data includes 200 well production data, and completion designs were collected from oil production wells in the Niobrara shale formation. The completion design parameters include the lateral length, the number of stages, the total injected proppant and slurry volumes, and the maximum treating pressure measured during the fracturing operations. The data set was randomly split into training and testing with a ratio of 75:25. Different machine learning methods were to predict EUR from the completion design including linear regression, random forest (RF), and decision tree (DT) in addition to gradient boosting regression (GBR). EUR prediction from the completion data showed a low accuracy. As result, an intermediate step of estimating the well IP30 (the initial well production rate for the first month) from the completion data was carried out; then, the IP30 and the completion design were used as input parameters to predict the EUR. The linear regression showed some linear relationship between the output and the inputs, where the EUR can be predicted with a linear relationship with an R-value of 0.84. In addition, a linear correlation was developed based on the linear regression model. Moreover, the other ML tools including RF, DT, and GBR presented high accuracy of EUR prediction with correlation coefficient (R) values between actual and predicted EUR from the ML model higher than 0.9. This study provides ML application with an empirical correlation to predict the EUR from the completion design parameters at an early time without the need for complex numerical simulation analysis. Unlike the available empirical DCA models that require several months of production to build a sound prediction of EUR, the main advantage of the developed models in this study is that it requires only an initial flow rate along with the completion design to predict EUR with high certainty.

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Section: Eur Estimation Methodsmentioning

confidence: 99%

RETRACTED ARTICLE: Data-driven EUR for multistage hydraulically fractured wells in shale formation using different machine learning methods

Ibrahim

Alarifi

Elkatatny

2023

J Petrol Explor Prod Technol

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“…Methods for assessing the original geological reserves and original recoverable reserves of gas reservoirs primarily include the material balance method, pressure-drop method, elastic two-phase method, decline curve analysis, and predictive modeling. Both the material balance method and pressure-drop method are based on the principle of mass conservation, estimating recoverable reserves by considering the relationships between gas production, water influx, and the changes in rock and fluid elastic expansion and pressure, using cumulative gas production and pressure data. − The elastic two-phase method evaluates the original geological reserves controlled by gas wells during the pseudosteady-state phase following the initiation of gas production, using the downhole flowing pressure and cumulative production. , The decline curve analysis is suitable for gas reservoirs entering the decline phase, rapidly estimating production and reserves by fitting historical production data. − While this method is relatively simple, it may not be suitable for new wells or complex reservoirs due to a lack of historical production data or the neglect of reservoir complexity.…”

Section: Introductionmentioning

confidence: 99%

A New Evaluation Method of Recoverable Reserves and Its Application in Carbonate Gas Reservoirs

Zhou,

Li,

et al. 2024

ACS Omega

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The propagation pattern of pressure drawdown effectively reflects the recoverable reserves range around the gas well and serves as a crucial basis for development strategies. However, it is not easy to detect the pressure propagation boundary near the producing well, especially in low-permeability reservoirs where the drainage radius is small. Physical simulation experiments can serve as a crucial method as the whole pressure profile and gas rate can be obtained in real time. Using long core plugs with permeabilities of 2.300 mD, 0.486 mD, and 0.046 mD, physical simulation experiments were carried out under varying initial water saturation (Swi) conditions of 0%, 20%, 40%, and 55% to observe the dynamic variations in pressure profiles of the core plugs during pressure depletion. Based on the material balance equation and pressure profile characteristics of the core plugs, a method for evaluating recoverable reserves within a well-spacing radius through laboratory experiments was proposed and performed. Mechanism analysis was conducted based on mercury injection tests, and suggestions for enhancing gas recovery were presented. Research findings indicate that lower permeability, higher initial water saturation, and higher abandonment gas rates result in reduced reserve utilization range and degree. Under abandoned gas rate conditions, for type I and II rocks, the pore radius is primarily distributed between 0.1 and 1 μm, the pressure drawdown can reach the well-spacing radius of 600 m, and the ultimate recovery efficiencies are more than 70.6%. For type III rocks, the pore radius mainly falls below 0.1 μm, the drainage radius is smaller than 10 m with Swi greater than 40%, and the ultimate recovery is below 10%. This paper provides an experimental method for recoverable reserves evaluation while formulating gas reservoir development strategies before well testing.

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“…Several DCA models have been introduced to forecast production tailored for unconventional wells. A common method is the stretched exponential production decline (SEPD) [19], in addition to the Arps hyperbolic decline with a "best-fit" hyperbolic decline exponent "b" value, but all are based on empirical observations of a particular scenario and have their own limitations, which can yield unreasonable EUR estimation [15,[19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning to Predict Estimated Ultimate Recovery for Multistage Hydraulically Fractured Wells in Niobrara Shale Formation

Ibrahim

Alarifi

Elkatatny

2022

Computational Intelligence and Neuroscience

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The completion design of multistage hydraulic fractured wells including the cluster spacing injected proppant and slurry volumes has shown a great influence on the well production rates and estimated ultimate recovery (EUR). EUR estimation is a critical process to evaluate the well profitability. This study proposes the use of different machine learning techniques to predict the EUR as a function of the completion design including the lateral length, the number of stages, the total injected proppant and slurry volumes, and the maximum treating pressure measured during the fracturing operations. A data set of 200 well production data and completion designs was collected from oil production wells in the Niobrara shale formation. Artificial neural network (ANN) and random forest (RF) techniques were implemented to predict EUR from the completion design. The results showed a low accuracy of direct prediction of the EUR from the completion design. Hence, an intermediate step of estimating the initial well production rate (Qi) from the completion data was carried out, and then, the Qi and the completion design were used as input parameters to predict the EUR. The ANN and RF models accurately predicted the EUR from the completion design data and the estimated Qi. The correlation coefficient (R) values between actual EUR and predicted EUR from the ANN model were 0.96 and 0.95 compared with 0.99 and 0.95 from the RF model for training and testing, respectively. A new correlation was developed based on the weight and biases from the optimized ANN model with an R value of 0.95. This study provides ML application with an empirical correlation to predict the EUR from the completion design parameters at an early time without the need for complex numerical simulation analysis. The developed models require only the initial flow rate along with the completion design to predict EUR with high certainty without the need for several months of production similar to the DCA models.

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A new rate-decline analysis of shale gas reservoirs: Coupling the self-diffusion and surface diffusion characteristics

Cited by 32 publications

References 47 publications

RETRACTED ARTICLE: Data-driven EUR for multistage hydraulically fractured wells in shale formation using different machine learning methods

RETRACTED ARTICLE: Data-driven EUR for multistage hydraulically fractured wells in shale formation using different machine learning methods

A New Evaluation Method of Recoverable Reserves and Its Application in Carbonate Gas Reservoirs

Application of Machine Learning to Predict Estimated Ultimate Recovery for Multistage Hydraulically Fractured Wells in Niobrara Shale Formation

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