Evaluating Fractured Well Performance Using Type Curves

1984 ◽  
Author(s):  
M.Y. Soliman ◽  
J.J. Venditto ◽  
G.L. Slusher
Keyword(s):  
Well Performance ◽  
Type Curves ◽  
Fractured Well

scholarly journals A production-based model for a fractured well in unconventional reservoirs

2020 ◽  
Author(s):  
Shaibu Mohammed ◽  
Prosper Anumah1 ◽  
Justice Sarkodie-kyeremeh ◽  
Emmanuel Acheaw
Keyword(s):  
Power Law ◽  
Numerical Models ◽  
Transient State ◽  
Production Performance ◽  
Unconventional Reservoirs ◽  
Well Performance ◽  
Type Curves ◽  
Stretched Exponential ◽  
Proposed Model ◽  
Fractured Well

Due to the depletion of conventional reservoirs and the high demand of energy, unconventional reservoirs will be relied on to supply the world’s energy for the foreseeable future. Unfortunately, modelling and analysis of these reservoirs have been very challenging because of their complex storage and flow mechanisms. Although analytical, semi-analytical and numerical models have been proposed, these models rely on simplifying assumptions and require several input parameters. In this paper, a production-based model is proposed to analyze and predict a fractured-well performance in unconventional reservoirs. The model assumes a power law with a stretched exponential cut-off. While the power-law term governs the transient-state period, the stretched exponential term, which is a superposition of exponential decays, governs the boundary-dominated flow period. As a result, the model is capable of matching both the transient state and boundary-dominated flow portions of the data. The model has been validated with a numerical data and applied to several field data; in addition, the model has been used to estimate P10, P50 and P90 values, as well as to develop P10, P50 and P90 type curves for the Barnett shale. These type curves will be useful for production forecasting of new wells in the field or analogue fields. Results of the model have been compared with existing models. The findings show that the proposed model yields relatively good reserve estimates, and predicts the future production performance of unconventional reservoirs not only during the transient-state period, but also the boundary-dominated flow period. The proposed model may contribute to the ongoing efforts to improve the analysis and forecasting of fractured-well performance in unconventional reservoirs.

The Use of Rate-Transient-Analysis Modeling To Quantify Uncertainties in Commingled Tight Gas Production-Forecasting and Decline-Analysis Parameters in the Alberta Deep Basin

2014 ◽  
Vol 17 (02) ◽  
pp. 209-219 ◽  
Author(s):  
H.. Luo ◽  
G.F.. F. Mahiya ◽  
S.. Pannett ◽  
P.. Benham
Keyword(s):  
Transient Analysis ◽  
Early Time ◽  
Gas Production ◽  
Tight Gas ◽  
Well Performance ◽  
Deep Basin ◽  
Type Curves ◽  
Production Forecasting ◽  
Rate Transient Analysis ◽  
Production History

Summary The evaluation of expected ultimate recovery (EUR) for tight gas wells has generally relied upon the Arps equation for decline-curve analysis (DCA) as a popular approach. However, it is typical in tight gas reservoirs to have limited production history that has yet to reach boundary-dominated flow because of the low permeability of such systems. Commingled production makes the situation even more complicated with multiboundary behavior. When suitable analogs are not available, rate-transient analysis (RTA) can play an important role to justify DCA assumptions for production forecasting. The Deep-basin East field has been developed with hydraulically fractured vertical wells through commingled production from multiple formations since 2002. To evaluate potential of this field, DCA type curves for various areas were established according to well performance and geological trending. Multiple-segment DCA methodology demonstrated reasonable forecasts, in which one Arps equation is used to describe the rapidly decreasing transient period in early time and another equation is used for boundary-dominated flow. However, a limitation of this approach is the uncertainty of the forecast in the absence of extended production data because the EUR can be sensitive to adjustments in some assumed DCA parameters of the second segment. In this paper, we used RTA to assess reservoir and fracture properties in multiple layers and built RTA-type well models around which uncertainty analyses were performed. The distributions of the model properties were then used in Monte Carlo analysis to forecast production and define uncertainty ranges for EUR and DCA parameters. The resulting forecasts and EUR distribution from RTA modeling generally support the DCA assumptions used for the type curves for corresponding areas of the field. The study also showed how the contribution from the various commingled layers changes with time. The proposed workflow provides a fit-for-purpose way to quantify uncertainties in tight gas production forecasting, especially for cases when production history is limited and field-level numerical simulation is not practicable.

scholarly journals Prediction of Hydraulic Fractured Well Performance Using Empirical Correlation and Machine Learning

2021 ◽  
Vol 44 (2) ◽  
pp. 131-145
Author(s):  
Kamal Hamzah ◽  
Amega Yasutra ◽  
Dedy Irawan
Keyword(s):  
Machine Learning ◽  
Hydraulic Fracturing ◽  
Petroleum Industry ◽  
Prediction Method ◽  
Empirical Correlation ◽  
Well Performance ◽  
Data Limitations ◽  
Medium Permeability ◽  
Water Cut ◽  
Fractured Well

Hydraulic fracturing has been established as one of production enhancement methods in the petroleum industry. This method is proven to increase productivity and reserves in low permeability reservoirs, while in medium permeability, it accelerates production without affecting well reserves. However, production result looks scattered and appears to have no direct correlation to individual parameters. It also tend to have a decreasing trend, hence the success ratio needs to be increased. Hydraulic fracturing in the South Sumatra area has been implemented since 2002 and there is plenty of data that can be analyzed to resolve the relationship between actual production with reservoir parameters and fracturing treatment. Empirical correlation approach and machine learning (ML) methods are both used to evaluate this relationship. Concept of Darcy's equation is utilized as basis for the empirical correlation on the actual data. The ML method is then applied to provide better predictions both for production rate and water cut. This method has also been developed to solve data limitations so that the prediction method can be used for all wells. Empirical correlation can gives an R2 of 0.67, while ML can gives a better R2 that is close to 0.80. Furthermore, this prediction method can be used for well candidate selection means.

scholarly journals Optimization of Fractured Well Performance of Horizontal Gas Wells

2007 ◽  
Author(s):  
Fellipe Vieira Magalhaes ◽  
Ding Zhu ◽  
Shahram Amini ◽  
Peter P. Valko
Keyword(s):  
Well Performance ◽  
Gas Wells ◽  
Fractured Well

scholarly journals Production decline type curves analysis of a finite conductivity fractured well in coalbed methane reservoirs

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 143-153 ◽  
Author(s):  
Mingqiang Wei ◽  
Ming Wen ◽  
Yonggang Duan ◽  
Quantang Fang ◽  
Keyi Ren
Keyword(s):  
Flow Regime ◽  
Coalbed Methane ◽  
Radial Flow ◽  
Production Model ◽  
Finite Conductivity ◽  
Type Curves ◽  
State Diffusion ◽  
Diffusion And Convection ◽  
Fractured Well ◽  
Advanced Production

AbstractProduction decline type curves analysis is one of the robust methods used to analyze transport flow behaviors and to evaluate reservoir properties, original gas in place, etc. Although advanced production decline analysis methods for several well types in conventional reservoirs are widely used, there are few models of production decline type curves for a fractured well in coalbed methane (CBM) reservoirs. In this work, a novel pseudo state diffusion and convection model is firstly developed to describe CBM transport in matrix systems. Subsequently, based on the Langmuir adsorption isotherm, pseudo state diffusion and convection in matrix systems and Darcy flow in cleat systems, the production model of a CBM well with a finite conductivity fracture is derived and solved by Laplace transform. Advanced production decline type curves of a fractured well in CBM reservoirs are plotted through the Stehfest numerical inversion algorithm and computer programming. Six flow regimes, including linear flow regime, early radial flow in cleat systems, interporosity flow regime, late pseudo radial flow regime, transient regime and boundary dominated flow regime, are recognized. Finally, the effect of relevant parameters, including the storage coefficient of gas in cleat systems, the transfer coefficient from a matrix system to the cleat system, the modified coefficient of permeability, dimensionless fracture conductivity and dimensionless reservoir drainage radius, are analyzed on type curves. This paper does not only enrich the production decline type curves model of CBM reservoirs, but also expands our understanding of fractured well transport behaviors in CBM reservoirs and guides to analyze the well's production performance.

Gas-Production-Data Analysis of Variable-Pressure-Drawdown/Variable-Rate Systems: A Density-Based Approach

2014 ◽  
Vol 17 (04) ◽  
pp. 520-529 ◽  
Author(s):  
Miao Zhang ◽  
Luis F. Ayala H.
Keyword(s):  
Data Analysis ◽  
Material Balance ◽  
Gas Production ◽  
Variable Pressure ◽  
Production Data ◽  
Variable Rate ◽  
Well Performance ◽  
Gas Well ◽  
Type Curves ◽  
Performance Forecasting

Summary This study demonstrates that production-data analysis of variable-bottomhole-flowing-pressure/variable-rate gas wells under boundary-dominated flow (BDF) is possible by use of a density-based approach. In this approach, governing equations are expressed in terms of density variables and dimensionless viscosity/compressibility ratios. Previously, the methodology was successfully used to derive rescaled exponential models for gas-rate-decline analysis of wells primarily producing at constant bottomhole pressure (Ayala and Ye 2013a, b; Ayala and Zhang 2013; Ye and Ayala 2013; Zhang and Ayala 2014). For the case of natural-gas systems experiencing BDF, gas-well-performance analysis has been made largely possible by invoking the concepts of pseudotime, normalized pseudotime, or material-balance pseudotime. The density-based methodology rigorously derived in this study, however, does not use any type of pseudotime calculations, even for variable-rate/variable-pressure-drawdown cases. The methodology enables straightforward original-gas-in-place calculations and gas-well-performance forecasting by means of type curves or straight-line analysis. A number of field and numerical case studies are presented to showcase the capabilities of the proposed approach.

Prediction of Hydraulic Fractured Well Performance Using Empirical Correlation and Machine Learning

2021 ◽  
Vol 44 (2) ◽  
pp. 141-152
Author(s):  
Kamal Hamzah ◽  
Amega Yasutra ◽  
Dedy Irawan
Keyword(s):  
Machine Learning ◽  
Hydraulic Fracturing ◽  
Petroleum Industry ◽  
Prediction Method ◽  
Empirical Correlation ◽  
Well Performance ◽  
Data Limitations ◽  
Medium Permeability ◽  
Water Cut ◽  
Fractured Well

Hydraulic fracturing has been established as one of production enhancement methods in the petroleum industry. This method is proven to increase productivity and reserves in low permeability reservoirs, while in medium permeability, it accelerates production without affecting well reserves. However, production result looks scattered and appears to have no direct correlation to individual parameters. It also tend to have a decreasing trend, hence the success ratio needs to be increased. Hydraulic fracturing in the South Sumatra area has been implemented since 2002 and there is plenty of data that can be analyzed to resolve the relationship between actual production with reservoir parameters and fracturing treatment. Empirical correlation approach and machine learning (ML) methods are both used to evaluate this relationship. Concept of Darcy's equation is utilized as basis for the empirical correlation on the actual data. The ML method is then applied to provide better predictions both for production rate and water cut. This method has also been developed to solve data limitations so that the prediction method can be used for all wells. Empirical correlation can gives an R2 of 0.67, while ML can gives a better R2 that is close to 0.80. Furthermore, this prediction method can be used for well candidate selection means.

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