Intelligent approaches for prediction of compressional, shear and Stoneley wave velocities from conventional well log data: A case study from the Sarvak carbonate reservoir in the Abadan Plain (Southwestern Iran)
Introduction
Compressional, shear and Stoneley wave velocities (Vp, Vs and Vst, respectively) are three fundamental parameters for hydrocarbon reservoir evaluation; including lithology indication, porosity calculation, identification of reservoir-fluids, estimation of permeability, fracture evaluation and geophysical/geomechanical studies. These parameters (especially Vs and Vst) are obtained directly from core analysis in the laboratory or dipole sonic imager (DSI) tools. Since laboratory methods are very expensive and time consuming; and conventional sonic tools cannot measure these parameters, attempts have been made to find new methods for wave velocity estimation. Many researchers have tried to predict Vs from well log data (e.g., Castagna et al., 1985, Castagna et al., 1993; Gassmann, 1951; Greenberg and Castagna, 1992; Han, 1989; Krief et al., 1990; Pickett, 1963; Rezaee and Applegate, 1997; Rezaee et al., 2006). Most of these studies have been carried out in sandstone reservoirs. Since carbonate rocks are considered as the major parts of the world's oil and gas reservoirs, more research is required on the physical characteristics of this type of reservoir. So far, several researchers have studied the application of intelligent systems in geosciences (e.g., Cuddy, 1998; Lim, 2005; Mohaghegh, 2000; Nikravesh et al., 2003; Rezaee et al., 2006, Rezaee et al., 2008; Saggaf and Nebrija, 2003).
The present study focuses on Vp, Vs and Vst prediction in a carbonate reservoir using genetic algorithms (GA), fuzzy logic (FL) and neuro-fuzzy (NF) techniques from well log data (LLD, RHOB and NPHI).
Some advantages of this study include the following:
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Generally, well logs can provide a continuous record over the entire well; thus, using well log data as input, Vp, Vs and Vst can be estimated over whole the well.
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Inputs (LLD, RHOB and NPHI) are available in most wells, and there is no need to gather additional data.
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The prediction techniques are relatively simple, cheap and quick.
Section snippets
Genetic algorithms (GA)
Optimization is a general tool used in numerous problems of engineering and sciences. GA are search algorithms developed by Holland (1975) which is based on the mechanics of natural selection and genetics to search through decision space for optimal solutions (Kaya, 2009).
In GA, a set of solutions are encoded in the form of a string, called a chromosome. A set of chromosomes (the initial population) is a potential solution to a problem, evaluated based on fitness function. The best chromosomes
An overview on Vp, Vs and Vst
Commonly, compressional and shear waves are referred to as body waves, while Stoneley wave is a type of surface wave and propagates along the mud formation interface within a wellbore. In compressional waves or P-waves, the motion of particles is always in the direction of wave propagation. In contrast, the motion of individual particles is always perpendicular to the direction of wave propagation, in shear waves or S-waves. The relation between Vp, Vs, Vst, density and elastic constants can be
Selection of appropriate input data
For selecting input data, we sought a logical relationship between inputs (well log data) and outputs (Vp, Vs and Vst). Sonic velocity depends on many factors; two of the main factors in carbonate rocks are porosity and pore type (Bakhorji and Schmitt, 2008; Eberli et al., 2003; Vanorio et al., 2008). In formation evaluation, neutron (NPHI) and density (RHOB) logs introduced as porosity logs and electrical resistivity logs relate to pore geometry and fluid type. Therefore NPHI, RHOB and LLD
Geological setting
The Abadan Plain located in the southwestern Zagros Mountains (southwestern Iran) is considered a major oil province of Iran; geologically it is a part of the northern Arabian plate (Fig. 6). The Sarvak Formation of the Bangestan Group is the most important oil reservoir in this region and is primarily composed of carbonate rocks. Based on Alavi studies (2004), the Sarvak Formation is composed of gray, resistant (cliff-forming) shallow-marine limestones (partially argillaceous and micritic and
Prediction of Vp, Vs and Vst
To construct intelligent models (GA, FL and NF models) for predicting sonic wave velocities, a total of 3030 data points were used. These data were divided into two groups. One group included 2047 data points used for constructing intelligent models, and the other included 983 data points used for model testing. In the following sections, the prediction of Vp, Vs and Vst (using intelligent models) are described.
Discussion and comparison of the output results with the empirical equations
The Sarvak carbonate Formation is the most important reservoir in the study region. This formation is equivalent to the Mishrif Formation of Iraq, which shows excellent reservoir conditions in reefal facies (Mostaghel and Afrassiabian, 2009). The lithology of the Sarvak Formation is predominantly carbonate with a very low content of shale (Fig. 21). Most wells drilled in this formation do not contain any Vs or Vst data. Therefore, constructed models can be applied for predicting these
Conclusions
This paper focuses on the prediction of Vp, Vs and Vst using intelligent techniques in a carbonate formation in southwestern Iran. Prediction in carbonate rocks is difficult because of diagenetic processes; however, intelligent systems give more reliable results. Intelligence systems, including GA, FL and NF, have different concepts and methodologies, but the obtained Vp, Vs and Vst from these techniques are promising. Therefore, using these methodologies (especially FL), Vp, Vs and Vst can be
Acknowledgements
The authors wish to thank Petroleum Engineering and Development Company (PEDEC) for sponsorship, data preparation and permission to publish the data. We are grateful to E. Heydari for his cooperation. We also appreciate helpful reviews by Dr. Steve Cuddy and an anonymous Computers & Geosciences reviewer.
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