Reservoir Characterization. Группа авторов

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      Preface

      An important step in exploration development, monitoring, and management a reservoir as well optimizing production and planning for post primary production decisions is reservoir characterization. Upon the completion of the preliminary task of reservoir characterization, and as we continue to produce from the reservoir or use different methods to stimulate it, many of its properties change. This requires updating the reservoir model, bringing up the concept of dynamic reservoir characterization. To achieve this goal, we incorporate the newly acquired petrophysical, seismic, micro seismic and production data. The updated model would be a better representative of the status of the reservoir. Both static reservoir properties, such as porosity, permeability, and facies type; and dynamic reservoir properties, such as pressure, fluid saturation, and temperature, needs to be updated as more field data become available.

      Among the reason for focusing on reservoir characterization is the fact based on the estimates by experts, more than 95% of the world’s oil production in the 21^st century will come from existing fields. This will require significant improvements in the current recovery rates of less than 50% in most reservoirs. Improved secondary and tertiary recovery through enhanced recovery of oil and gas require by better understanding and monitoring of the reservoir will be an important element of the much-needed increase in the recovery factor. Increased production will be made possible only through effective dynamic reservoir characterization.

      We need to recognize the fact that reservoir characterization is a multidisciplinary field. It attempts to describe petroleum deposits and the nature of the rocks that contain hydrocarbons using a variety of data types. Reservoir characterization relies on expertise from petroleum engineers, geologists, geochemists, petrophysicists, and of course geophysicists, The integration of information from these fields, with the aid of advanced data analysis techniques as well as artificial intelligence (AI) based methods will make our reservoir models more accurate and the updating process much faster.

This book will provide a comprehensive body of technical material on different aspects of reservoir characterization. It is divided into 7 parts: Part 1 is an introductory chapter covering the general concepts, of reservoir characterization. It includes an overview of what is meant by reservoir characterization as it is applied in different stages of its life, from exploration to post primary production stages. It also highlights the challenges of data integration of different data types, the previously mentioned dynamic reservoir characterization, and reservoir stimulation for enhanced oil (or gas) recovery.

      Part 2 deals with general issues on reservoir characterization and anomaly detection. It is comprised of 7 chapters on different related topics such as: (1) Comparison between estimated shear wave velocity and elastic modulus at in situ pressure condition (2) Anomaly detection (3) geochemical analysis on characterization of carbonate source-derived hydrocarbons, (4) MWD mud pulse telemetry, (5) Use of Monte Carlo clustering to detect geologic anomalies, (6) Gas-sand predictors using dissimilarity analysis, and (7) Fluid flow tests distorted by wellbore storage effects. Part 3 is dedicated to reservoir permeability detection, being one of the most important reservoir properties. What are covered here are three different techniques. Two of them involves use of two different machine learning techniques to predict permeability, namely, exponential/multiplicative and Monte Carlo/committee machines. The other chapter discusses geoscience criteria identifying high gas permeability zones.

      One of the reasons for reservoir characterization is to assess the recoverable reserves in the reservoir. Part 4 addresses reserves evaluation and decision-making issues. The first chapter of this part discusses foundation for science-based decision making, using data from the Gulf of Mexico. The next chapter in Part 4 investigates decline trends in a reservoir using Bootstrap and Monte Carlo modeling. This Part concludes with a typical production, reserves, and valuation method used in an oil and gas company.

      Given the tremendous success with the development and production from shale reservoirs over the last 2 decades, Part 5 is dedicated to the unconventional reservoirs. The chapters in Part 5 include: (1) Optimization of Gas-Drilling in Unconventional Tight-Sand Reservoirs, (2) Predicting the Fluid Temperature Profile in Drilling Gas Hydrates Reservoirs, (3) Distinguishing between brine and gas-saturated shaly formations, and (4) Influence of shale mechanical properties on water content effects.

Part 6 is about enhanced oil recovery. It covers EOR with hydrophilic nanofluids, as well as CO₂-EOR Flow Simulation for the Tensleep Formation using 3D seismic data. Part 7 is the concluding section, highlighting new advances in reservoir characterization. It discusses the recent application of machine learning in reservoir characterization. It also discusses the future trends in reservoir characterization and the impact of data explosion associated with the real time reservoir monitoring and reservoir surveillance. It also describes how the “Big Data” concepts and data analytics techniques will play a role in the next generation reservoir characterization technology developments.

      It should be understood reservoir characterization is an evolving technology. It is our hope that this volume will be a meaningful addition to the current body of literature and will help pave the way for further advances on the subject matter in the future.

       Fred Aminzadeh

      Santa Barbara, California

      September 22, 2021