INTRODUCTION: FIELD APPLICATIONS OF INTELLIGENT COMPUTING TECHNIQUES

The natural complexities of petroleum reservoir systems continue to provide a challenge to geoscientists. The absence of reliable data often leads to an inadequate understanding of reservoir behaviour and consequently to poor performance predictions. Although this is an ongoing problem and one which may be difficult to resolve without additional data and/or investment, it is important to pursue the best possible solutions using whatever data is readily available. Data integration, and risk and uncertainty assessment, have become the major issues in reservoir characterization and improved oil recovery. In past decades, classical data processing tools and physical models were adequate for the solution of relatively "simple" geological problems. However because of the uncertainties which are inherent in geological data, the challenge we now face now is not just to predict the presence of hydrocarbons, but rather to quantify the confidence of reservoir predictions. We are increasingly being faced with more and more complex problems, and reliance on current technologies based on conventional methodologies is becoming less satisfactory. Intelligent (or "soft") computing consists of a suite of emerging technologies in data and knowledge processing. These include neural networks, fuzzy logic, evolutionary computing and advanced statistical tools. The formulation and application of intelligent techniques have increased exponentially over the past few years. Unlike conventional (or "hard") computing, intelligent techniques are tolerant of imprecision, uncertainty and partial truth. They are also tractable, robust, efficient and inexpensive. Intelligent techniques are bound to play a key role in earth sciences in the future (Zadeh and Aminzadeh, 1995; Aminzadeh, 1999; Tamhane et al., 2000). This is mainly due to the fact that physical models cannot describe geological and physical phenomena accurately but rely on the interpretation of data. Many conventional modeling techniques rely purely on data and occasionally on indirect knowledge which can sometimes be unrelated to reservoir sedimentology and depositional characteristics.