A Detecting Technique for Production Rate Decline-Curve Analysis With Residual Plots

1991 ◽  
Author(s):  
Zhigang Chen
2018 ◽  
Vol 141 (1) ◽  
Author(s):  
Jiazheng Qin ◽  
Shiqing Cheng ◽  
Youwei He ◽  
Yang Wang ◽  
Dong Feng ◽  
...  

Nowadays, production performance evaluation of a multifractured horizontal well (MFHW) has attracted great attention. This paper presents a mathematical model of an MFHW with considering segmented fracture (SF) for better evaluation of fracture and reservoir properties. Each SF consists of two parts: fracture segment far from wellbore (FSFW) and fracture segment near to wellbore (FSNW) in segmented fracture model (SFM), which is different from fractures consists of only one segment in common fracture model (CFM). Employing the source function and Green's function, Newman's product method, Duhamel principle, Stehfest inversion algorithm, and Laplace transform, production solution of an MFHW can be obtained using SFM. Total production rate is mostly contributed from FSNW rather than FSFW in many cases; ignoring this phenomenon may lead to obvious erroneous in parameter interpretation. Thus, clear distinctions can be found between CFM and SFM on the compound type curves. By using decline curve analysis (DCA), the influences of sensitive parameters (e.g., dimensionless half-length, dimensionless production rate, conductivity, and distance between SF) on compound type curves are analyzed. The results of sensitivity analysis are benefit of parameter estimation during history matching.


2000 ◽  
Vol 3 (06) ◽  
pp. 525-533 ◽  
Author(s):  
J. Ansah ◽  
R.S. Knowles ◽  
T.A. Blasingame

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.


2016 ◽  
Vol 5 (2) ◽  
pp. 33-43
Author(s):  
Guntur Setiawan

Decline curve analysis often used to determine remaning reserves in a reservoir. To analyze with this method, the decline type curve from production period (trend) must be known. To determine decline type curve, in this paper will use two methods there are: trial error & x2 chisquare test and decline type curve matching. Both of methods have different way to determine decline type curve. Trial error done by making tabulation and calculation, while decline type curve matching done by overlay. The calculation are aimed to determine decline type curve, Remaining Reserves (RR) and Estimate Ultimate Recovery (EUR). Analysis done by taking sample of data well X which is a new well produced since September 2013 till the last data is Febuary 2016. First step of this study done by making type curve from equations and assumptionin literature then plot production rate vs time and choose production period (trend) to be analyzed. After that, determine decline type curve by trial error and decline type curve matching and do forecast until get remaining reserves and estimated ultimate recovery if economic limit rate production known. From calculation of both methods, resulted exponential decline type curve. For the error method obtained RR  41322,3 STB and EUR 240328,9 STB, while for decline type curve matching obtained RR 40534,2 STB and EUR 239540,8 STB


2015 ◽  
Vol 50 (1) ◽  
pp. 29-38 ◽  
Author(s):  
MS Shah ◽  
HMZ Hossain

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


1989 ◽  
Author(s):  
L. Turki ◽  
J.A. Demski ◽  
A.S. Grader

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