Principles of Simulation Applied to Oilfield Venture Analysis With Systems Approach
- Tarkeshwar Kumar (Indian School of Mines)
- Document ID
- Society of Petroleum Engineers
- Journal of Petroleum Technology
- Publication Date
- October 1986
- Document Type
- Journal Paper
- 1,111 - 1,112
- 1986. Society of Petroleum Engineers
- 4.2 Pipelines, Flowlines and Risers, 3 Production and Well Operations, 5.2.1 Phase Behavior and PVT Measurements, 1.6 Drilling Operations
- 0 in the last 30 days
- 75 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 1.00|
|SPE Non-Member Price:||USD 2.00|
Summary. The application of the systems approach to problem solving in general and to the Monte Carlo simulation technique in particular is discussed. The use and development of a simulation model, with examples from oil industry operations, are also presented.
The Monte Carlo simulation technique is generally used for evaluating uncertainty and involves a number of input variables, each one having an estimated probability distribution and one or more interrelationships among these variables. Oil industry operations are carried out under conditions of uncertainty. Exploration, drilling, development, and workovers face a high degree of uncertainty in their outcome. The systems approach, used with the complex and uncertain financial situation of oilfield ventures, provides a quick, more reliable solution.
System Concept and Approach
The systems approach can be applied when any type of problem - economic, engineering, social, or behavioral - is problem - economic, engineering, social, or behavioral - is being solved. In the oil field, it can be related to drilling, production, pipeline, oil reservoir, processing, or marketing production, pipeline, oil reservoir, processing, or marketing systems. In the systems approach of problem solving, a conscious attempt is made to understand the relationship between various parts of the system and to find alternative solutions after the parts of the system and to find alternative solutions after the objectives, constraints, and restraints are identified. Essentially, the systems approach is based on the conviction that before any functional solution is implemented, one must examine its ultimate effect on other functional areas and on the entire system. After the system is conceptualized, the systems approach is used in 10 steps to solve the problem systematically. These steps constitute the basic structure of the decision model. 1. Identifying the problem. This requires conscious and systematic recognition of the problem based on a scientific approach and systems orientation. The ultimate purpose is the precise definition of the problem. precise definition of the problem. 2. Formulating the goals and objectives. 3. Listing the constraints and restraints on the basis of assumptions and environment, such as financial, legal, and operational factors. 4. Devising various alternative solutions to the problem with the factors present. 5. Developing the decision model or running the simulation model when that is the chosen method. 6. Determining the solution or the simulated behavior of the system under various options with the decision model. 7. Selecting the acceptable solution or behavior of the system on the basis of the acceptable criteria. 8. Implementing the decision. 9. Monitoring the various control parameters. 10. Evaluating the collected information during monitoring, and improving or redefining the system by changing control parameters or by critically examining the validity of the stated parameters or by critically examining the validity of the stated goals and objectives.
Monte Carlo Simulation Model
This simulation is based on the probability of the occurrence of a number of parameters that constitute a solution. It is assumed with good reason that in the possible outcome of a given system, it is unnecessary for all favorable or unfavorable parameters to occur at one time. The outcome is based on experimentation - i.e., running the model many times to simulate the probability distribution of outcomes. Therefore, the Monte Carlo simulation model has been recognized as a very powerful tool for oil exploration ventures, reservoir parameter estimations, and financial analyses of all types of projects that involve risk and uncertainty.
Development of the Simulation Model
Development of the decision model for simulation involves the identification of (1) the system that is being studied (e.g., reservoir hydrocarbon systems); (2) components of the system (fluid properties, reservoir rock boundary, and production data); (3) criterion variables (e.g., recoverable oil); (4) decision or controllable variables (e.g., rock volume, porosity, water saturation, and oil FVF's); and (5) functional or system relationships. In each simulation experiment, the elements of the decision model must be fully specified before the model is run. Development of the simulation model has to be based on the best available knowledge of the actual system.
|File Size||181 KB||Number of Pages||2|