Book Description

Accurate, high-resolution, three-dimensional (3D) reservoir characterization can provide substantial benefits for effective oilfield management. By doing so, the predictive reliability of reservoir flow models, which are routinely used as the basis for investment decisions involving hundreds of millions of dollars and designed to recover millions of barrels of oil, can be significantly improved. Even a small improvement in incremental recovery for high-value assets can result in important contributions to bottom-line profitability. Today's standard practice for developing a 3D reservoir description is to use seismic inversion techniques. These techniques make use of geostatistics and other stochastic methods to solve the inverse problem, i.e., to iteratively construct a likely geologic model and then upscale and compare its acoustic response to that actually observed in the field. This method has several inherent flaws, such as: (1) The resulting models are highly non-unique; multiple equiprobable realizations are produced, meaning (2) The results define a distribution of possible outcomes; the best they can do is quantify the uncertainty inherent in the modeling process, and (3) Each realization must be run through a flow simulator and history matched to assess it's appropriateness, and therefore (4) The method is labor intensive and requires significant time to complete a field study; thus it is applied to only a small percentage of oil and gas producing assets. A new approach to achieve this objective was first examined in a Department of Energy (DOE) study performed by Advanced Resources International (ARI) in 2000/2001. The goal of that study was to evaluate whether robust relationships between data at vastly different scales of measurement could be established using virtual intelligence (VI) methods. The proposed workflow required that three specific relationships be established through use of artificial neural networks (ANN's): core-to-log, log-to-crosswell seismic, and crosswell-to-surface seismic. One of the key attributes of the approach, which should result in the creation of high resolution reservoir characterization with greater accuracy and with less uncertainty than today's methods, is the inclusion of borehole seismic (such as crosswell and/or vertical seismic profiling--VSP) in the data collection scheme. Borehole seismic fills a critical gap in the resolution spectrum of reservoir measurements between the well log and surface seismic scales, thus establishing important constraints on characterization outcomes. The results of that initial study showed that it is, in fact, feasible to establish the three critical relationships required, and that use of data at different scales of measurement to create high-resolution reservoir characterization is possible. Based on the results of this feasibility study, in September 2001, the DOE, again through ARI, launched a subsequent two-year government-industry R & D project to further develop and demonstrate the technology. The goals of this project were to: (1) Make improvements to the initial methodology by incorporating additional VI technologies (such as clustering), using core measurements in place of magnetic resonance image (MRI) logs, and streamlining the workflow, among others. (2) Demonstrate the approach in an integrated manner at a single field site, and validate it via reservoir modeling or other statistical methods.