Production data analysis of tight gas condensate reservoirs

Behmanesh, Hamid; Hamdi, Hamidreza; Clarkson, Christopher R.

doi:10.1016/j.jngse.2014.11.005

Cited by 54 publications

(15 citation statements)

References 21 publications

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“…Future production profiles and estimated ultimate recovery (EUR) of shale gas wells are obtained by such methods as reservoir simulation [2], hydraulic fracturing modeling, rate transient analysis (RTA), and decline curve analysis (DCA). However, the reservoir simulation implies high uncertainty in representing flow behavior due to reservoir properties, complexity of reservoir, absorption or desorption flow, and variation of fracture permeability [3][4][5][6][7]. It is a time-consuming and a challenge task.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on Supervised Learning Models for Productivity Forecasting of Shale Reservoirs Based on a Data-Driven Approach

Han

Jung²,

Kwon

2020

Applied Sciences

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Due to the rapid development of shale gas, a system has been established that can utilize a considerable amount of data using the database system. As a result, many studies using various machine learning techniques were carried out to predict the productivity of shale gas reservoirs. In this study, a comprehensive analysis is performed for a machine learning method based on data-driven approaches that evaluates productivity for shale gas wells by using various parameters such as hydraulic fracturing and well completion in Eagle Ford shale gas field. Two techniques are used to improve the performance of the productivity prediction machine learning model developed in this study. First, the optimal input variables were selected by using the variables importance method (VIM). Second, cluster analysis was used to analyze the similarities in the datasets and recreate the machine learning models for each cluster to compare the training and test results. To predict productivity, we used random forest (RF), gradient boosting tree (GBM), and support vector machine (SVM) supervised learning models. Compared to other supervised learning models, RF, which is applied with the VIM, has the best prediction performance. The retraining model through cluster analysis has excellent predictive performance. The developed model and prediction workflow are considered useful for reservoir engineers planning of field development plan.

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Section: Introductionmentioning

confidence: 99%

Comparative Study on Supervised Learning Models for Productivity Forecasting of Shale Reservoirs Based on a Data-Driven Approach

Han

Jung²,

Kwon

2020

Applied Sciences

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“…In cases where these two compressibilities are comparable, one should account for pressure support from rock compressibility in the material-balance equation, as well. The average pressure in the DOI tends to be a constant value for constant flowing pressure, as demonstrated in the work of Behmanesh et al (2015b). This observation is fundamentally based on a mathematical derivation.…”

Section: Appendix A: Determination Of Doi Maximummentioning

confidence: 88%

“…The data form a straight line after 100 days of production, indicating that linear flow is the dominant flow regime. Using the slope of the plot and the appropriate equation for gas/condensate reservoirs (Behmanesh et al 2015b), the total x f ffiffi ffi k p is calculated (the sum of x f ffiffi ffi k p from each stage) to be 21.5 ftÁmd 0.5 from the unit impulse method. The x f ffiffi ffi k p calculated from Wattenbarger's DOI formulation and transient/ boundary-dominated flow intersection method DOI are 20.1 ftÁmd 0.5 (6% error with respect to unit impulse method) and 18.9 ftÁmd 0.5 (12% error with respect to unit impulse method), respectively.…”

Section: Discussion: Impact Of Doi Constantmentioning

confidence: 99%

See 1 more Smart Citation

Impact of Distance-of-Investigation Calculations on Rate-Transient Analysis of Unconventional Gas and Light-Oil Reservoirs: New Formulations for Linear Flow

Behmanesh

Clarkson

Tabatabaie

et al. 2015

Journal of Canadian Petroleum Technology

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Long-term transient linear flow of hydraulically fractured vertical and horizontal wells completed in tight/shale gas wells has historically been analyzed by use of the square-root-of-time plot. Pseudovariables are typically used for compressible fluids to account for pressure-dependence of fluid properties. Recently, a corrected pseudotime has been introduced for this purpose, in which the average pressure in the distance of investigation (DOI) is calculated with an appropriate material-balance equation. The DOI calculation is therefore a key component in the determination of the linear-flow parameter (product of fracture half-length and square root of permeability, x f ffiffi ffi k p) and the calculation of contacted fluid in place. Until now, the DOI for transient linear flow has been determined empirically, and may not be accurate for all combinations of fluid properties and operating conditions.In this work, we have derived the DOI equations analytically for transient linear flow under constant-flowing-pressure and -rate conditions. For the first time, rigorous methodologies have been used for this purpose. Two different approaches were used: the maximum rate of pressure response (impulse concept) and the transient/boundary-dominated flow intersection method. The two approaches resulted in constants in the DOI equation that are much different from previously derived versions for the constantflowing-pressure case. The accuracy of the new equations was tested by analyzing synthetic production data from a series of fine-grid numerical simulations. Single-phase oil and gas cases were analyzed; pseudovariable alteration for pressure-dependent porosity and permeability was required in the analysis.The calculated linear-flow parameters, determined from our new DOI formulations for the constant-flowing-bottomhole-pressure (FBHP) case, and the input values to numerical simulation, are in good agreement. Of the two new DOI-calculation methods provided, the maximum rate of pressure response (unit impulse method) provides more accurate results. Finally, a field case was analyzed to determine the impact of DOI formulations on derivations of the linear-flow parameter from field data.Linear-flow analysis on the basis of the DOI calculations presented in this work is significantly improved over previous formulations for constant FBHP.

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“…The focus of this paper was on modeling of capacitance and resistance for the single-phase fluid systems. However, the approach can be extended for multiphase flow situations by employing proper pseudo variable (Behmanesh et al, 2015) definitions to linearize the equations. Therefore the single-phase liquid analogy can still be applied.…”

Section: Discussionmentioning

confidence: 99%

A Novel Method for Performance Analysis of Compartmentalized Reservoirs

Shahamat¹,

Hamdi

Mattar³

et al. 2015

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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-This paper presents a simple analytical model for performance analysis of compartmentalized reservoirs producing under Constant Terminal Rate (CTR) and Constant Terminal Pressure (CTP). The model is based on the well-known material balance and boundary dominated flow equations and is written in terms of capacitance and resistance of a production and a support compartment. These capacitance and resistance terms account for a combination of reservoir parameters which enable the developed model to be used for characterizing such systems. In addition to considering the properties contrast between the two reservoir compartments, the model takes into account existence of transmissibility barriers with the use of resistance terms.The model is used to analyze production performance of unconventional reservoirs, where the multistage fracturing of horizontal wells effectively creates a Stimulated Reservoir Volume (SRV) with an enhanced permeability surrounded by a non-stimulated region. It can also be used for analysis of compartmentalized conventional reservoirs. The analytical solutions provide type curves through which the controlling reservoirs parameters of a compartmentalized system can be estimated. The contribution of the supporting compartment is modeled based on a boundary dominated flow assumption. The transient behaviour of the support compartment is captured by application of "distance of investigation" concept. The model shows that depletion of the production and support compartments exhibit two unit slopes on a log-log plot of pressure versus time for CTR. For CTP, however, the depletions display two exponential declines. The depletion signatures are separated by transition periods, which depend on the contribution of the support compartment ( i.e. transient or boundary dominated flow).The developed equations can be implemented easily in a spreadsheet application, and are corroborated with the use of a numerical simulation. The study provides a new and nonconventional insight into the use of production data as an aid in the understanding of hydraulically fractured tight formations and compartmentalized conventional reservoirs.

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Production data analysis of tight gas condensate reservoirs

Cited by 54 publications

References 21 publications

Comparative Study on Supervised Learning Models for Productivity Forecasting of Shale Reservoirs Based on a Data-Driven Approach

Comparative Study on Supervised Learning Models for Productivity Forecasting of Shale Reservoirs Based on a Data-Driven Approach

Impact of Distance-of-Investigation Calculations on Rate-Transient Analysis of Unconventional Gas and Light-Oil Reservoirs: New Formulations for Linear Flow

A Novel Method for Performance Analysis of Compartmentalized Reservoirs

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