SeisView provides a basic framework to construct an exploration and production integrated workstation. The programmer or the explorationist constructs new applications or new processing sequences by taking preprogrammed functional objects and graphically connecting them to form a new application program without writing any new code in a traditional high-level language. These objects are interactive objects (menus, icons, etc.), data objects (plane, trace, etc.), processing program objects (NMO, Mute, etc.).
For the programmer this system provides an object-oriented environment to describe (1) the rules of the dialogue between the system and the user, (2) the rules to access the programs, (3) the relations between the different programs.
For the explorationist this system will provide an homogeneous, integrated, easy-to-use workstation. SeisView includes a planner which advises the user what to do next, and how to use some processing tools, then guides the user in building the processing tasks. The planner controls the processing history.
Figures & Tables
“So far as the laws of mathematics refer to reality they are not certain; and so far as they are certain, they don’t refer to reality.” This quote from Albert Einstein explains the difficulties associated with a mathematical description of many subjective rules used by human experts in reaching a decision. This fact, combined with the multidisciplinary nature of oil exploration, has prevented wide spread use of expert systems in the oil industry. Alternative approaches, that can account for uncertainty and are better equipped for integrating data and rules from different disciplines, should be used to allow expert systems to become viable tools in our applications.
This book examines a diverse set of petroleum exploration problems that properly designed expert systems can help solve. Chapter 1 provides an extensive review of current state-of-the-art expert systems as pertains to oil industry problems. Emphasis is given to how uncertainty and inexactness of data and rules from different disciplines could be handled by expert systems. The chapter suggests that fuzzy logic, evidential reasoning, and neural networks will prove essential in the design of many expert systems that are capable of solving more practical exploration problems. The problem of automatic picking of stacking velocities, using a rule based system, is addressed in Chapter 2. The expert system automates the task of picking the extreme of the velocity spectrum. The system incorporates common sense rules to distinguish primary reflection related peaks in the spectrum from those related to multiples and noise.