Sun Advanced Production Decline Analysis and Application

1. Auflage 2015
ISBN: 978-0-12-802627-4
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 352 Seiten

In recent years, production decline-curve analysis has become the most widely used tool in the industry for oil and gas reservoir production analysis. However, most curve analysis is done by computer today, promoting a 'black-box' approach to engineering and leaving engineers with little background in the fundamentals of decline analysis. Advanced Production Decline Analysis and Application starts from the basic concept of advanced production decline analysis, and thoroughly discusses several decline methods, such as Arps, Fetkovich, Blasingame, Agarwal-Gardner, NPI, transient, long linear flow, and FMB. A practical systematic introduction to each method helps the reservoir engineer understand the physical and mathematical models, solve the type curves and match up analysis, analyze the processes and examples, and reconstruct all the examples by hand, giving way to master the fundamentals behind the software. An appendix explains the nomenclature and major equations, and as an added bonus, online computer programs are available for download. - Understand the most comprehensive and current list of decline methods, including Arps, Fetkovich, Blasingame, and Agarwal-Gardner - Gain expert knowledge with principles, processes, real-world cases and field examples - Includes online downloadable computer programs on Blasingame decline type curves and normalized pseudo-pressure of gas wells

Hedong Sun, PhD, SPE member, born in 1973, professional senior engineer, earned his PhD degree from Xi'an Jiaotong University in 2004. Since 2004, he has been a Research Engineer in Research Institute of Petroleum Exploration and Development of Petrochina. He has about 25 years of reservoir engineering experience with a focus on well test analysis and production data analysis. He has published over 60 papers in peer-reviewed journals and SPE conferences. He is an author of 4 books published by Elsevier, including Advanced Production Decline Analysis and Application, Well Test Analysis for Multilayered Reservoir with Formation Crossflow, Dynamic Well Testing in Petroleum Exploration and Development, among others.

Autoren/Hrsg.

Sun, Hedong

Leseproben

1

Fundamentals of Advanced Production Decline Analysis

Abstract

Advanced production decline analysis (APDA), or rate transient analysis or production analysis, is a procedure to process and interpret the daily production data of wells for obtaining parameters of such wells or reservoirs. This chapter introduces the history of APDA based on filtration theory, its similarities to and differences with well test analysis. In addition, this chapter also introduces several concepts related to the APDA.

Keywords

advanced production decline analysis

history

well test analysis

basic concept

Advanced production decline analysis (APDA), or rate transient analysis or production analysis, is a procedure to process and interpret the daily production data of wells for obtaining parameters of such wells or reservoirs. This chapter introduces the history of APDA based on filtration theory, its similarities to and differences with well test analysis. In addition, this chapter also introduces several concepts related to the APDA.

1.1. History of Advanced Production Decline Analysis (APDA)

At the middle and later stages of reservoir development, daily production data of a well becomes the focus for reservoir analysis. They can be used to forecast the most probable well life, evaluate well production in the future, and determine the interwell communication relation and infill potential. Currently, the production decline analysis technique consists of the conventional Arps (1945) method, classical Fetkovich (1980) type curve matching method, modern Palacio and Blasingame (1993) and Agarwal et al. (1998) type curve matching methods and FMB (1998) reservoir engineering method.

Extrapolating the characteristic trend of some variables of a well can be helpful for our jobs. As to a well, the simplest and the most easily available variable is its production. If the flow rate versus time or cumulative production curve is plotted and extrapolated, the ultimate cumulative production can be obtained. The trend or mathematical relations indicated by the entire rate history of a well can be used to forecast the production performance in the future, which is referred to as the conventional Arps (1945) decline curve analysis method. This method magnificently describes the production decline laws of well at a constant bottom hole flowing pressure (BHFP) and in the completely boundary-dominated flow period. The greatest advantage of this method is that formation parameters are not necessarily obtained. On the other hand, it is not suitable for data analysis from the transient flow stage.

A variety of interpretations may occur for the data of one well or one reservoir, mostly resulting from the experiences of appraisers or the difference of appraisal targets. Just as pointed out by Ramsay (1968), “Some new papers contributing to the decline curve analysis were published in 1964-1968, but there was hardly any new technique.” Slider (1968) developed a matching method applicable to the production-time data, which is similar to the log–log type curve matching method in well test analysis and uncovers a new direction for decline curve analysis. Because this method was quick and easy, Ramsay extensively used it to determine the distribution of decline exponent b in the appraisal of more than 200 wells. Gentry (1972) plotted three Arps decline curves on one chart to match the decline data of wells, where the dimensionless time was defined the same as with the Fetkovich (1980) method, and the dimensionless production was the reciprocal of relevant variables in Fetkovich method.

Arps type curve can only be used to analyze the data of a boundary-dominated flow period. Fetkovich (1980), on the basis of homogeneous bounded formation transient filtration theory, introduced the transient flow formula in well test analysis to the decline analysis, so that the Arps type curve is extended to the transient flow period prior to boundary-dominated flow, and the transient rate decline curve and the Arps rate decline equation are organically combined. In this way, the production decline laws and the effect of boundary are intuitively shown, and a set of relatively complete log–log production decline curve matching analysis method similar to well test analysis is developed. The greatest advantage of the method is its ability to reliably determine whether the production is in a transient flow period or in a boundary-dominated flow period.

Both Arps and Fetkovich methods assume that the BHFP is constant to analyze the production data without considering the change of gas pressure–volume–temperature (PVT) charateristics with pressure. Palacio and Blasingame (1993) introduced the pseudo-pressure normalized production (q/pp) and the material balance pseudo-time tca to develop the type curve, which considered the production at variable BHFP and the gas PVT changing with formation pressure.

Agarwal et al. (1998) used the relations of pseudo-pressure normalized production (q/pp), material balance pseudo-time tca, and dimensionless parameters in well test analysis to develop the Agarwal-Gardner production decline analysis. Owing to the different definitions of dimensionless quantity, the early part of the curve is more discrete than the Blasingame chart and thus is in favor of reducing the ambiguity of matching analysis.

Both Blasingame and Agarwal-Gardner methods used the pseudo-pressure normalized production (q/pp) and the material balance pseudo-time tca to create type curve, while the NPI (normalized pressure integral) method (Blasingame et al., 1989) used the production normalized pressure integral to analyze the data available, which was not affected by the scatter of data.

Palacio and Blasingame (1993) and Agarwal et al. (1998) type curve matching analysis methods introduced pseudo-time (or material balance pseudo-time) and production normalized pseudo-pressure (pseudo-pressure normalized production) to deal with variable BHFP, variable rate, and change of gas PVT with pressure. They used the flow rate integral, flow rate integral derivative, cumulative production–time, and flow rate–cumulative production type curves as the auxiliary matching analysis curves to reduce the ambiguity of interpretation results.

Her-Yuan and Teufel (2000) developed the method on the basis of Fetkovich’s findings, and presented the linear flow characteristic curve usually occurring in low-permeability tight gas reservoir. Wattenbarger and El-Banbi (1998) and his students combined the linear flow model and the curve matching analysis method in well test analysis to present the analysis method for long-term linear flow production data of gas well in low-permeability tight gas reservoirs. Pratikno et al. (2003) developed the type curve and analysis method of a vertical fracture well. Yong-Xin Han (2006) also made helpful research on the long-term linear flow of low-permeability fracture wells.

Mattar et al. (1998, 2006) and Agarwal et al. (1998) suggested using the “flow (dynamic) material balance” method to analyze the production data, and conducted detailed discussion on the calculation of material balance time. This method is simple and easy. Mattar and Anderson (2003) believes that there is no one universal production data analysis method that can meet all types of reservoirs, and the best way to eliminate analysis errors is to synthetically use all analysis methods and consider flowing pressure data.

Over nearly a century, the APDA technique has evolved with several advances, including target to be analyzed, that is, from purely production data to both flow rate and pressure data; analytic model, that is, from no model to both analytical model and numerical model; analytic method, that is, from the empirical Arps method to the log-log method represented by Blasingame; applicable conditions, that is, from simple constant pressure production data to variable pressure and variable rate data; and the estimation parameters, that is, from only cumulative production to many parameters such as formation permeability, skin factor, dynamic reserves and drainage area, as well as interwell communication and infill potential.

1.2. Similarities and Differences between Production Decline Analysis and Well Test Analysis

As to dynamic reservoir description, APDA and well test analysis are combined to appraise the reservoir where the well is located, with the high precision pressure data acquired from transient well test and the dynamic data like pressure and flow rate obtained in production test and actual production, and based on understandings obtained from static geologic data. The parameters to be appraised include reservoir permeability, skin factor, dynamic reserves, drainage radius, fault sealing, and advancing range of edge water. As two major techniques for dynamic reservoir description, the APDA and the well test analysis have both specific and common features. They should be well combined and constrained with each other, so as to minimize the uncertainties of parameters interpretation. The similarities and differences between them are shown in Table 1.1.

Table 1.1

Comparison of the PA and WTA (after Oliver et al.,...