Application of Geostatistics for Reservoir Characterization-Accomplishments and Challenges

Abstract

Geostatistics has been used to improve the reservoir characterization process for the last fifteen years. This article briefly discusses the accomplishments so far, and discusses the future challenges. What Is a Reservoir Characterization Process? Reservoir characterization is a process of integrating various qualities and quantities of data in a consistent manner to describe reservoir properties of interest at inter well locations. Appropriate weight should be given to the quality and the scale of the data, and data should be integrated such that we can predict the future performance of the reservoir. Our goal in reservoir characterization is not to seek the truth about reservoir; instead, to build a reasonable reservoir model which is adequate to predict the future performance. The model will not only be dependent on the type of data available, but it should also be dependent on the type of flow process we are trying to simulate. More complex the flow process (e.g., CO2 process), more detailed will be the reservoir description; more simple the flow process (e.g., dry gas reservoir), simpler will be the reservoir description. Reservoir description process is not new; since the first oil discovery, oil companies have used all the available data techniques to describe the reservoir so that the next well to be drilled will be based on more information than a prior well. However, several changes have taken place in the last fifteen years. Some of these changes are listed below:We have better and faster computers so that we can handle more complex algorithms, and can generate detailed reservoir descriptions in a reasonable amount of time. PC revolution has brought the computer power to small players, making it feasible for even small operators to harness the power of detailed reservoir description processes.Several new algorithms have been developed in the last fifteen years which allow integration of various types of data in an easier fashion.As the reservoirs get more mature, there is more need to describe the reservoir in more details so as to locate the remaining hydrocarbons.There is an increasing recognition that representation of heterogeneities is as important as representing correct physics in the flow simulators. What Is Geostatistics? Geostatistics is based on a simple principle that geological data are spatially correlated. This spatial correlation is quantified and is utlilized to determine the weights assigned to the nearby samples to estimate the value at the unsampled location. Geostatistics has been used in mining industry for several years. Its use in petroleum industry is of relatively recent origin. To address the unique problems encountered in petroleum engineering, several new geostastical techniques have been established in the past several years. Some of these techniques are discussed below. Accomplishments to Date Several papers have been published in the literature to demonstrate the application of geostatistics. It will be impossible to list all the successes of various geostatistical techniques. Instead, an effort is made to highlight the accomplishments which are widely used and embraced by oil companies.

Integration of Adaptive Neuro-Fuzzy Inference System, Neural Networks and Geostatistical Methods for Fracture Density Modeling

“…Kelkar (2000). Yarus and Chambers (2006) provided an overview for application of geostatistics for reservoir characterization.…”

Vertical resolution enhancement of petrophysical Nuclear Magnetic Resonance (NMR) log using ordinary kriging

“…It is a method that is based on two criteria of minimization of variance and unbiased condition [14]. Kriging is the best linear unbiased estimator, with the exception of the Simple Kriging technique [15]. This estimator, defined as:…”

Applied Geostatisitcs Analysis for Reservoir Characterization Based on the SGeMS (Stanford Geostatistical Modeling Software)

“…The properties involved in the reservoir description may include the permeability, porosity, saturation, thickness, faults and fractures, rock facies and rock characteristics [1]. The reservoir characterization gives a proper global reservoir prediction of these diverse reservoir properties as a function of special based on the limited local information, which could be generally understood as a process to describe the reservoir heterogeneity and solve the upscaling problem.…”

“…The flow units data was described at different well locations by analyzing several geological cross sections throughout the field, including top and thickness, porosity, permeability (in log form) from cores and logs. There are totally ten flow unit data which are ordered from the top to bottom of the reservoir and each of them has overall gross thickness ranging from 46 -85 ft with the mean of 65 ft. Flow units one, two and eight to ten are located at the top and bottom of the reservoir which are less productive, while flow unit three to seven are in the middle of the reservoir, which are most productive and provide best petrophysical properties [1].…”