Production data analysis of shale gas using fractal model and fuzzy theory: Evaluating fracturing heterogeneity

You, Xu-Tao; Jia, Chun‐Sheng; Jun, Li; Liao, Xinyi; Zheng, Aiwei

doi:10.1016/j.apenergy.2019.05.049

Cited by 17 publications

(4 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These reservoirs are developed by drilling a horizontal well with multistage hydraulic fractures [14]. The hydraulic fractures create artificial permeability, allowing flow to the wellbore [14,15]. The flow is transient in these fractures and could last for a long time before reaching the BDF [16][17][18].…”

Section: Overview Of Dca Modelsmentioning

confidence: 99%

Probabilistic Decline Curve Analysis: State-of-the-Art Review

et al. 2023

View full text Add to dashboard Cite

The decline curve analysis (DCA) technique is the simplest, fastest, least computationally demanding, and least data-required reservoir forecasting method. Assuming that the decline rate of the initial production data will continue in the future, the estimated ultimate recovery (EUR) can be determined at the end of the well/reservoir lifetime based on the declining mode. Many empirical DCA models have been developed to match different types of reservoirs as the decline rate varies from one well/reservoir to another. In addition to the uncertainties related to each DCA model’s performance, structure, and reliability, any of them can be used to estimate one deterministic value of the EUR, which, therefore, might be misleading with a bias of over- and/or under-estimation. To reduce the uncertainties related to the DCA, the EUR could be assumed to be within a certain range, with different levels of confidence. Probabilistic decline curve analysis (pDCA) is the method used to generate these confidence intervals (CIs), and many pDCA approaches have been introduced to reduce the uncertainties that come with the deterministic DCA. The selected probabilistic type of analysis (i.e., frequentist or Bayesian), the used DCA model(s), the type and the number of wells, the sampling technique of the data or the model’s parameters, and the parameters themselves undergo a probability distribution, and these are the main differences among all of these approaches and the factors that determine how each approach can quantify the uncertainties and mitigate them. In this work, the Bayesian and frequentist approaches are deeply discussed. In addition, the uncertainties of DCA are briefly discussed. Further, the bases of the different probabilistic analyses are explained. After that, 15 pDCA approaches are reviewed and summarized, and the differences among them are stated. The study concludes that Bayesian analysis is generally more effective than frequentist analysis, though with narrower CIs. However, the choice of DCA model and sampling algorithm can also affect the bounds of the CIs and the calculation of the EUR. Moreover, the pDCA approach is recommended for quantifying uncertainties in DCA, with narrower CIs that indicate greater effectiveness. However, the computational time and the number of iterations in sampling are also considered critical factors. That is why various assumptions and modifications have been made in the pDCA approaches, including the assumption of a certain probability distribution for the sampled parameters to improve their reliability of reserve estimation. The motivation behind this research was to present a full state-of-the-art review of the pDCA and the latest developments in this area of research.

show abstract

Section: Overview Of Dca Modelsmentioning

confidence: 99%

Probabilistic Decline Curve Analysis: State-of-the-Art Review

et al. 2023

View full text Add to dashboard Cite

show abstract

“…However, there exists a propensity for entrapment in local optima under specific circumstances. FLSs possess the capability to model uncertainty and imprecision within data, offering interpretability through linguistic rules [14,15]. The formulation of pertinent fuzzy rules necessitates domain-specific expertise, potentially impeding the handling of complex non-linear relationships inherent in the data [16].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Evaluation of Shale Oil Reservoir Quality

Tian,

Fu,

Liu

et al. 2024

Processes

View full text Add to dashboard Cite

To enhance the accuracy of the comprehensive evaluation of reservoir quality in shale oil fractured horizontal wells, the Pearson correlation analysis method was employed to study the correlations between geological parameters and their relationship with production. Through principal component analysis, the original factors were linearly combined into principal components with clear and specific physical meanings, aiming to eliminate correlations among factors. Furthermore, Gaussian membership functions were applied to delineate fuzzy levels, and the entropy weight method was used to determine the weights of principal components, establishing a fuzzy comprehensive evaluation model for reservoir quality. Without using principal component analysis, the correlation coefficient between production and evaluation results for the 40 wells in the Cangdong shale oil field was only 0.7609. However, after applying principal component analysis, the correlation coefficient increased to 0.9132. Field application demonstrated that the average prediction accuracy for the cumulative oil production per kilometer of fractured length over 12 months for the 10 applied wells was 91.8%. The proposed comprehensive evaluation method for reservoir quality can guide the assessment of reservoir quality in shale oil horizontal wells.

show abstract

“…Analytical and numerical methods are used to simulate fluid flow in porous medium. Analytical models use solutions to simple reservoir behaviour [6][7][8][9][10][11] while numerical simulation ones use solutions to more complex reservoir behaviour [12][13][14]. Straightline analysis methods are analogous to those used in pressure transient analysis [15].…”

Section: Introductionmentioning

confidence: 99%

Modern Decline Curve Analysis of Unconventional Reservoirs: A Comparative Study Using Actual Data

Wahba

Khattab

Tantawy

et al. 2022

Journal of Petroleum and Mining Engineering

View full text Add to dashboard Cite

Petroleum consumption increases around the world and production of conventional reservoirs can't cover the increased demand. So, producing unconventional resources is an imperative necessity.Unconventional resources are characterized by very low permeability. Drilling horizontal wells in these resources and completed them with multiple hydraulic fractures make the reservoir. Hydraulic fractures work as paths for hydrocarbon to flow toward the wellbore to achieve an economic production rate. Production behaviour of these wells is characterized by long-term transient flow followed by boundarydominated flow. Many decline curve analysis models have been developed to simulate this behaviour, but none of them can capture all flow-regime types. This paper reviewed the most popular and used decline curve analysis models: Arps model, power-law exponential model, stretched exponential production decline model, T-model, logistic growth model, Duong model, Yu-Miocevic model and extended exponential decline curve. This paper summarized the origins, derivations and assumptions of these eight models. This paper also presents a comparative study of these models using production data from unconventional gas and oil reservoirs. To facilitate conducting this study, the eight decline curve analysis models were programmed in a software application written in python language. This software application calibrated models' parameters to production data using trust region reflective algorithm. The value of estimated ultimate recovery predicted using this software application is consistent with that predicted using the linear flow analysis model. The comparative study can serve as a guideline for petroleum engineers to determine when to use each model.

show abstract

Production data analysis of shale gas using fractal model and fuzzy theory: Evaluating fracturing heterogeneity

Cited by 17 publications

References 48 publications

Probabilistic Decline Curve Analysis: State-of-the-Art Review

Probabilistic Decline Curve Analysis: State-of-the-Art Review

A Comprehensive Evaluation of Shale Oil Reservoir Quality

Modern Decline Curve Analysis of Unconventional Reservoirs: A Comparative Study Using Actual Data

Contact Info

Product

Resources

About