Progress of the research on productivity prediction methods for stimulated reservoir volume (SRV)-fractured horizontal wells in unconventional hydrocarbon reservoirs

Ren, Lan; Su, Yuliang; Zhan, Shiyuan; Meng, Fankun

doi:10.1007/s12517-019-4376-2

Cited by 13 publications

(9 citation statements)

References 57 publications

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“…The production of horizontal wells is always at a low level during production. The production of conventional fractured horizontal wells is higher at the initial stage, while that of volumetric fractured horizontal wells is higher at the initial stage, and then decreases with production (Ren et al, 2019). The reason is that in horizontal wells, the use range is small, only near the horizontal wells.…”

Section: Figure 1 Fracture Elliptic Seepagementioning

confidence: 96%

The characteristics of unconventional tight oil reservoir and its modification technology and productivity prediction

et al. 2021

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The zonal productivity prediction model of volume fractured horizontal wells in tight reservoirs is established in this study based on the physical model of volume fractured horizontal wells to study the near-well seepage law of volume fractured horizontal wells in unconventional tight reservoirs. The conformal transformation and iteration method are used to solve the problem. Compared with Fan Zifei's and Ning Zhengfu's method, the optimization of fractured horizontal wells' production parameters is studied. The effects of starting pressure gradient and fracture cluster number on horizontal wells' productivity with volume fracturing are analyzed. Multivariate regression analysis is used to rank the importance of influencing factors. The results show that the starting pressure gradient has the most significant impact on capacity. The application example of volume fractured horizontal wells in Lucaogou tight reservoir shows that the zonal productivity model established in this study can be used to predict the initial productivity of volume fractured horizontal wells. Thus, through theoretical analysis and numerical simulation, the productivity prediction model of horizontal wells with volume fracturing has certain guiding significance for the effective development of tight reservoirs.

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Section: Figure 1 Fracture Elliptic Seepagementioning

confidence: 96%

The characteristics of unconventional tight oil reservoir and its modification technology and productivity prediction

et al. 2021

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“…This network configures into an axisymmetric elliptical cylindrical shape along the wellbore, with the major and minor axes of the ellipse being 2a and 2b, respectively. Building on this conceptual framework, a physical model known as the two-dimensional elliptic orthogonal line network model (Cheng et al, 2013;Shi et al, 2014;Ren et al, 2015;Li et al, 2019;Ren et al, 2019;Zheng et al, 2023) has been developed to encapsulate the propagation dynamics of fracture networks with SRV fracturing in unconventional reservoirs. A visual representation of this model is delineated in Figure 1.…”

Section: Mathematical Characterization Model 21 Physical Model and As...mentioning

confidence: 99%

Quantitative characterization of stimulated reservoir volume (SRV) fracturing effects in naturally fractured unconventional hydrocarbon reservoirs

Ren,

Dou,

Dong

et al. 2024

Front. Earth Sci.

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Stimulated reservoir volume (SRV) fracturing has become the most efficient technology in the treatment of unconventional hydrocarbon reservoir formations. This process aims to optimize well productivity by establishing an intricate network of fractures that integrate hydraulic and natural fractures, distal to the wellbore, thereby amplifying the contact area with the subterranean formations and fracture systems. This study introduces a quantitative framework designed to characterize the fracturing effects within naturally fractured unconventional hydrocarbon reservoirs. Leveraging existing fracturing treatment designs and production performance data, the study formulates a mathematical model of the complex fracture network, predicated on the principle of material balance. The model comprehensively accounts for the development degree of natural fractures, the morphological impact of stress differentials on the fracture network, and the imbibition displacement effects of the fracturing fluids. The model’s accuracy is verified through an integration with microseismic monitoring data and an enhanced understanding of reservoir development. Building upon this foundation, the study quantitatively dissects the impact of various engineering parameters on the efficacy of SRV fracturing. The proposed quantitative characterization method is adept for widespread application across multiple wells in oil and gas fields, offering a distinct advantage for the swift and precise assessment of SRV fracturing outcomes in naturally fractured unconventional hydrocarbon reservoirs. The research method, which is based on readily accessible fracturing construction data and is more convenient, can to a certain extent improve the efficiency of hydraulic fracturing evaluation work.

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“…However, the simulation requires extensive geological and engineering data on the reservoir, which may not always be available. A deep understanding of the geology and engineering aspects of the complex reservoir is also challenging for people without professional knowledge [23]. Moreover, the assumptions made in the simulation model can impact the predictive accuracy.…”

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

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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.

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Progress of the research on productivity prediction methods for stimulated reservoir volume (SRV)-fractured horizontal wells in unconventional hydrocarbon reservoirs

Cited by 13 publications

References 57 publications

The characteristics of unconventional tight oil reservoir and its modification technology and productivity prediction

The characteristics of unconventional tight oil reservoir and its modification technology and productivity prediction

Quantitative characterization of stimulated reservoir volume (SRV) fracturing effects in naturally fractured unconventional hydrocarbon reservoirs

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

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