A Complement to Decline Curve Analysis

SPE Journal ◽  
2021 ◽  
pp. 1-11
Author(s):  
Randy D. Hazlett ◽  
Umer Farooq ◽  
Desarazu K. Babu
Keyword(s):  
Reservoir Characterization ◽  
Curve Analysis ◽  
Sensitivity Analyses ◽  
Well Performance ◽  
Crossover Point ◽  
Decline Curve Analysis ◽  
Matrix Permeability ◽  
Decline Curve ◽  
Constant Rate ◽  
Diffusivity Equation

Summary Decline curve analysis (DCA) has been the mainstay in unconventional reservoir evaluation. Because of the extremely low matrix permeability, each well is evaluated economically for ultimate recovery as if it were its own reservoir. Classification and normalization of well potential is difficult because of ever-changing stimulation total contact area and a hyperbolic curve fit parameter that is disconnected from any traditional reservoir characterization descriptor. A new discrete fracture model approach allows direct modeling of inflow performance in terms of fracture geometry, drainage volume shape, and matrix permeability. Running such a model with variable geometrical input to match the data in lieu of standard regression techniques allows extraction of a meaningful parameter set for reservoir characterization, an expected outcome from all conventional well testing. Because the entirety of unconventional well operation is in transient mode, the discrete fractured well solution to the diffusivity equation is used to model temporal well performance. The analytical solution to the diffusivity equation for a line source or a 2D fracture operating under constrained bottomhole pressure consists of a sum of terms, each with exponential damping with time. Each of these terms has a relationship with the constant rate, semisteady-state solution for inflow, although the well is not operated with constant rate, nor will this flow regime ever be realized. The new model is compared with known literature models, and sensitivity analyses are presented for variable geometry to illustrate the depiction of different time regimes naturally falling out of the unified diffusivity equation solution for discrete fractures. We demonstrate that apparent hyperbolic character transitioning to exponential decline can be modeled directly with this new methodology without the need to define any crossover point. The mathematical solution to the physical problem captures the rate transient functionality and any and all transitions. Each exponential term in the model is related to the various possible interferences that may develop, each occurring at a different time, thus yielding geometrical information about the drainage pattern or development of fracture interference within the context of ultralow matrix permeability. Previous results analyzed by traditional DCA can be reinterpreted with this model to yield an alternate set of descriptors. The approach can be used to characterize the efficacy of evolving stimulation practices in terms of geometry within the same field and thus contribute to the current type curve analyses subject to binning. It enables the possibility of intermixing of vertical and horizontal well performance information as simply gathering systems of different geometry operating in the same reservoir. The new method will assist in reservoir characterization and evaluation of evolving stimulation technologies in the same field and allow classification of new type curves.

scholarly journals Pre-Drilling Production Forecasting of Parent and Child Wells Using a 2-Segment Decline Curve Analysis (DCA) Method Based on an Analytical Flow-Cell Model Scaled by a Single Type Well

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1525
Author(s):  
Ruud Weijermars ◽  
Kiran Nandlal
Keyword(s):  
Cell Model ◽  
Flow Cell ◽  
Curve Analysis ◽  
Treatment Quality ◽  
Well Performance ◽  
Reservoir Simulations ◽  
Decline Curve Analysis ◽  
Production Forecasting ◽  
Well Spacing ◽  
Decline Curve

This paper advances a practical tool for production forecasting, using a 2-segment Decline Curve Analysis (DCA) method, based on an analytical flow-cell model for multi-stage fractured shale wells. The flow-cell model uses a type well and can forecast the production rate and estimated ultimate recovery (EUR) of newly planned wells, accounting for changes in completion design (fracture spacing, height, half-length), total well length, and well spacing. The basic equations for the flow-cell model have been derived in two earlier papers, the first one dedicated to well forecasts with fracture down-spacing, the second one to well performance forecasts when inter-well spacing changes (and for wells drilled at different times, to account for parent-child well interaction). The present paper provides a practical workflow, introduces correction parameters to account for acreage quality and fracture treatment quality. Further adjustments to the flow-cell model based 2-segment DCA method are made after history matching field data and numerical reservoir simulations, which indicate that terminal decline is not exponential (b = 0) but hyperbolic (with 0 < b< 1). The timing for the onset of boundary dominated flow was also better constrained, using inputs from a reservoir simulator. The new 2-segment DCA method is applied to real field data from the Eagle Ford Formation. Among the major insights of our analyses are: (1) fracture down-spacing does not increase the long-term EUR, and (2) fracture down-spacing of real wells does not result in the rate increases predicted by either the flow-cell model based 2-segment DCA (or its matching reservoir simulations) with the assumed perfect fractures in the down-spaced well models. Our conclusion is that real wells with down-spaced fracture clusters, involving up to 5000 perforations, are unlikely to develop successful hydraulic fractures from each cluster. The fracture treatment quality factor (TQF) or failure rate (1-TQF) can be estimated by comparing the actual well performance with the well forecast based on the ideal well model (albeit flow-cell model or reservoir model, both history-matched on the type curve).

A Semi-Analytic (p/z) Rate-Time Relation for the Analysis and Prediction of Gas Well Performance

2000 ◽  
Vol 3 (06) ◽  
pp. 525-533 ◽  
Author(s):  
J. Ansah ◽  
R.S. Knowles ◽  
T.A. Blasingame
Keyword(s):  
Production Rate ◽  
Gas Flow ◽  
Oil And Gas Industry ◽  
Flow Equation ◽  
Curve Analysis ◽  
Reservoir Pressure ◽  
Well Performance ◽  
Decline Curve Analysis ◽  
Decline Curve ◽  
Cumulative Production

Summary In this paper we present a rigorous theoretical development of solutions for boundary-dominated gas flow during reservoir depletion. These solutions were derived by directly coupling the stabilized flow equation with the gas material balance equation. Due to the highly nonlinear nature of the gas flow equation, pseudo pressure and pseudotime functions have been used over the years for the analysis of production rate and cumulative production data. While the pseudo pressure and pseudotime functions do provide a rigorous linearization of the gas flow equation, these transformations do not provide direct solutions. In addition, the pseudotime function requires the average reservoir pressure history, which in most cases is simply not available. Our approach uses functional models to represent the viscosity-compressibility product as a function of the reservoir pressure/z-factor (p/z) profile. These models provide approximate, but direct, solutions for modeling gas flow during the boundary-dominated flow period. For convenience, the solutions are presented in terms of dimensionless variables and expressed as type curve plots. Other products of this work are explicit relations for p/z and Gp(t). These solutions can be easily adapted for field applications such as the prediction of rate or cumulative production. We also provide verification of our new flow rate and pressure solutions using the results of numerical simulation and we demonstrate the application of these solutions using a field example. Introduction We focus here on the development and application of semi-analytic solutions for modeling gas well performance¾with particular emphasis on production rate analysis using decline type curves. Our emphasis on decline curve analysis arises both from its usefulness in viewing the entire well history, as well as its familiarity in the industry as a straightforward and consistent analysis approach. More importantly, the approach does not specifically require reservoir pressure data (although pressure data are certainly useful). Decline curve analysis typically involves a plot of production rate, qg and/or other rate functions (e.g., cumulative production, rate integral, rate integral derivative, etc.) vs. time (or a time-like function) on a log-log scale. This plot is matched against a theoretical model, either analytically as a functional form or graphically in the form of type curves. From this analysis formation properties are estimated. Production forecasts can then be made by extrapolation of the matched data trends. The specific formation parameters that can be obtained from decline curve analysis are original gas in place (OGIP), permeability or flow capacity, and the type and strength of the reservoir drive mechanism. In addition, we can establish the future performance of individual wells, and the estimated ultimate recovery (EUR). Attempts to theoretically model the production rate performance of gas and oil wells date as far back as the early part of this century. In 1921, a detailed summary of the most important developments in this area was documented in the Manual for the Oil and Gas Industry.1 Several efforts2,3 were made over the years immediately thereafter, and probably the most significant contribution towards the development of the modern decline curve analysis concept is the classic paper by Arps,2 written in 1944. In this work Arps presented a set of exponential and hyperbolic equations for production rate analysis. Although the basis of Arps' development was statistical (and therefore empirical), these historic results have found widespread appeal in the oil and gas industry. The continuous use of the so-called "Arps equations" is primarily due to the explicit form of the relations, which makes these equations quite useful for practical applications. The next major development in production decline analysis technology occurred in 1980, when Fetkovich4 presented a unified type curve which combined the Arps empirical equations with the analytical rate solutions for bounded reservoir systems.

scholarly journals Evaluation of natural gas production optimization in Kailashtila gas field in Bangladesh using decline curve analysis method

2015 ◽  
Vol 50 (1) ◽  
pp. 29-38 ◽  
Author(s):  
MS Shah ◽  
HMZ Hossain
Keyword(s):  
Production System ◽  
Gas Production ◽  
Curve Analysis ◽  
Production Optimization ◽  
Reservoir Pressure ◽  
Gas Field ◽  
Wellhead Pressure ◽  
Decline Curve Analysis ◽  
Decline Curve ◽  
Overall Performance

Decline curve analysis of well no KTL-04 from the Kailashtila gas field in northeastern Bangladesh has been examined to identify their natural gas production optimization. KTL-04 is one of the major gas producing well of Kailashtila gas field which producing 16.00 mmscfd. Conventional gas production methods depend on enormous computational efforts since production systems from reservoir to a gathering point. The overall performance of a gas production system is determined by flow rate which is involved with system or wellbore components, reservoir pressure, separator pressure and wellhead pressure. Nodal analysis technique is used to performed gas production optimization of the overall performance of the production system. F.A.S.T. Virtu Well™ analysis suggested that declining reservoir pressure 3346.8, 3299.5, 3285.6 and 3269.3 psi(a) while signifying wellhead pressure with no changing of tubing diameter and skin factor thus daily gas production capacity is optimized to 19.637, 24.198, 25.469, and 26.922 mmscfd, respectively.Bangladesh J. Sci. Ind. Res. 50(1), 29-38, 2015

Decline Curve Analysis in Composite Reservoirs

1989 ◽  
Author(s):  
L. Turki ◽  
J.A. Demski ◽  
A.S. Grader
Keyword(s):  
Curve Analysis ◽  
Decline Curve Analysis ◽  
Decline Curve
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