A Petrophysical Decision Support System

Oil and gas reservoirs are located in the earth’s crust at depths of several kilometers, and when located offshore, in water depths of a few meters to a few kilometers. Consequently, the gathering of critical information such as the presence and type of hydrocarbons, size of the reservoir and the physical properties of the reservoir such as the porosity of the rock and the permeability is a key activity in the oil and gas industry.

Pre-development methods to gather information on the nature of the reservoirs range from gravimetric, 2D and 3D seismic to the drilling of exploration and appraisal boreholes. Additional information is obtained while a field is developed through data acquisition in new development wells drilled to produce hydrocarbons, time-lapse seismic surveys and in-well monitoring of how the actual production of hydrocarbons affects physical properties such as the pressure and temperature. The purpose of information gathering is to decide which reservoirs can be developed economically, and how to adapt the means of development best to the particular nature of a reservoir.

The early measurements acquired in exploration, appraisal and development boreholes are a crucial component of the information gathering process. These measurements are typically obtained from tools on the end of a wireline that are lowered into the borehole to measure the rock and fluid properties of the formation. Their is a vast range of possible measurement tools [28]. Some options are very expensive and may even risk other data acquisition options. In general acquiring all possible data imposes too great an economic burden on the exploration, appraisal and development. Hence data acquisition options must be exercised carefully bearing in mind the learnings of already acquired data and general hydrocarbon field knowledge.

Also important is a clear understanding of what data can and cannot be acquired later and the consequences of having an incorrect understanding of the nature of a reservoir on the effectiveness of its development.

Making the right data acquisition decisions, as well as the best interpretation of information obtained in boreholes forms one of the principle tasks of petrophysicists. The efficiency of a petrophysicist executing her/his task is substantially influenced by the ability to gauge her/his experience to the issues at hand. Efficiency is hampered when a petrophysicists experience level is not yet fully sufficient and by the rather common circumstance that decisions to acquire particular types of information or not must be made in a rush, at high costs and shortly after receiving other information that impact on that very same decision. Mistakes are not entirely uncommon and almost always painful. In some cases, non essential data is obtained at the expense of extremely high cost, or essential data is not obtained at all; causing development mistakes that can jeopardize the amount of hydrocarbon recoverable from a reservoir and induce significant cost increases.

The overall effectiveness of petrophysicists is expected to improve using a decision support system (DSS). In practice a DSS can increase the petrophysicists’ awareness of low probability but high impact cases and alleviate some of the operational decision pressure.

In cooperation with Shell E&P, SNN has developed a DSS tool based on a Bayesian network and an efficient sampler for inference. The main tasks of the application is the estimation of compositional volume fractions in a reservoir on the basis of measurement data. In addition it provides insight in the effect of additional measurements. Besides an implementation of the model and the inference, the tool contains graphical user interface in which the user can take different views on the sampled probability distribution and on the effect of additional measurements.

In the remainder of this section, we will describe the Bayesian network approach for the DSS tool. We focus on our modeling and inference approach. More details are described elsewhere [5].

 
Source
< Prev   CONTENTS   Source   Next >