Reservoir Characterization and Modeling Supported by Expert Knowledge Systems

Abstract

Reservoir Characterization and Modeling Supported by Expert Knowledge Systems L.M. Surguchev; L.M. Surguchev Rogaland Research Search for other works by this author on: This Site Google Scholar A.B. Zolotukhin; A.B. Zolotukhin Rogaland U. Center Search for other works by this author on: This Site Google Scholar R.B. Bratvold R.B. Bratvold IBM EPAC Search for other works by this author on: This Site Google Scholar Paper presented at the European Petroleum Computer Conference, Stavanger, Norway, May 1992. Paper Number: SPE-24279-MS https://doi.org/10.2118/24279-MS Published: May 24 1992 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Surguchev, L.M., Zolotukhin, A.B., and R.B. Bratvold. "Reservoir Characterization and Modeling Supported by Expert Knowledge Systems." Paper presented at the European Petroleum Computer Conference, Stavanger, Norway, May 1992. doi: https://doi.org/10.2118/24279-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE European Petroleum Computer Conference Search Advanced Search SPE MembersAbstractDecisions concerning development and depletion of petroleum reservoirs must be made under uncertainty in the decision supporting information. The incomplete knowledge of the reservoir characteristics contributes significantly to this uncertainty. In addition, and this is particularly true for improved oil recovery (IOR) projects, the uncertainty in the operational environment makes hard to confirm and substantiate research efforts.To address these issues a multicriterion model is introduced. The model is coupled with a reservoir simulator and is used to estimate the applicability and efficiency of various field development strategies. The multicriterion model used here applies the methods of interval theory and fuzzy set logic. It permits the integration of geophysical data, the potential efficiency of various [OR methods and other resource requirements. A pre-modelling multicriterion analysis is used to optimize field scale reservoir simulations for IOR methods. Particular field case scenarios show how the reservoir description information and IOR experience and knowledge can be used for decision making support. This work demonstrates that a first-order screening decision making system combined with a field scale reservoir model can be used to further optimize the recovery while reducing the costs and uncertainty in the decision making.IntroductionThe development of oil and gas fields requires a solution of multicomponent problems. Many of the parameters influencing the optimal recovery are given by nature and are hard to quantify due to the limited number of measurable samples. Recovery of oil by traditional technologies doesn't meet modern levels of efficiency, leaving behind more than half of OOIP. Implementation of IOR methods requires considerable resources such as additional use of energy, expensive oil displacing agents, specialized equipment and skilled personnel. The uncertainties and uncompleteness existing in the reservoir characterization used to be acceptable with the level of expenditures of traditional oil recovery technologies. This is not the case for expensive and demanding IOR methods [I].Multicriterion procedures of a system analysis provide tools for exploring efficiency of possible alternatives and existing knowledge databases[2]. APPLICABILITY OF IOR METHODSOil and gas reservoirs represent complex fluid flow systems with a high degree of uncertainty it, the definition of the specific physical parameters of the media. The statistical analysis of available stochastic data and parameters is often limited by their insufficient quantity.P. 183^ Keywords: application, reservoir characterization, upstream oil & gas, brent reservoir, artificial intelligence, injection, evaluation, surguchev, applicability, oil recovery Subjects: Reservoir Fluid Dynamics, Improved and Enhanced Recovery, Flow in porous media, Waterflooding This content is only available via PDF. 1992. Society of Petroleum Engineers You can access this article if you purchase or spend a download.