Conditioning reservoir models to dynamic data - A forward modeling perspective

Conditioning Reservoir Models To Dynamic Data - A Forward Modeling Perspective Sanjay Srinivasan; Sanjay Srinivasan University of Calgary Search for other works by this author on: This Site Google Scholar Jef Caers Jef Caers Stanford University Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Annual Technical Conference and Exhibition, Dallas, Texas, October 2000. Paper Number: SPE-62941-MS https://doi.org/10.2118/62941-MS Published: October 01 2000 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Srinivasan, Sanjay, and Jef Caers. "Conditioning Reservoir Models To Dynamic Data - A Forward Modeling Perspective." Paper presented at the SPE Annual Technical Conference and Exhibition, Dallas, Texas, October 2000. doi: https://doi.org/10.2118/62941-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search nav search search input Search input auto suggest search filter All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Annual Technical Conference and Exhibition Search Advanced Search AbstractIn order to predict accurately future production performance, a reservoir model should reflect the actual patterns of permeability connectivity (flow paths and barriers). Information about such patterns of connectivity is carried by flow response data recorded at wells. However, the flow response data are influenced by many factors other than permeability connectivity such as boundary conditions and fluid property variations. This paper presents a neural network-based procedure for filtering the information related to the permeability field in flow response data. The flow response data is modeled as specific multiple point averages of permeability values in the neighborhood of the well. Such multiple point averages allow accounting for the spatial connectivity of the permeability field. The superiority of such multiple point averages over single point permeability averages for representing flow response data is demonstrated over several reservoir examples.The ultimate quest is to integrate the permeability connectivity information contained in the flow response data into the numerical reservoir models. This amounts to ascertain that the permeability numerical models identify the previous multiple point averages. A Markov chain Monte Carlo simulation algorithm is implemented to perform this identification. Alternative equiprobable permeability fields are generated which, in addition to reproducing the production data, conform to a prior model for the spatial variability of the permeability field. The results demonstrate that flow simulation on the simulated permeability fields do indeed match historic well test data accurately. More importantly, future reservoir performance predictions are rendered more accurate.IntroductionSpatial variations exhibited by the reservoir permeability field have an immediate impact on the fluid producing characteristics of the reservoir. The relationship between a reservoir response, say the well pressure pw(u't) at location u' and time t, and the permeability field k(u); u ? Reservoir, is complex and does vary with both location u' and time t. A reservoir simulator maps the permeability field to the observed well response i.e. it represents a transfer function TFw(k(u); u? Reservoir; t) defined as:Equation 1The mapping TFw is unique in that given a permeability field, the response at the wells can be computed uniquely using TFw. If the permeability field is not fully known, it is modeled with a suite of L equi-probable realizations k(u); =1, . . .L. All these realizations represent accurate models of the reservoir if application of the tranfer function TFw would yield a simulated well response close to the observed value:Since flow and future productivity is controlled by the spatial connectivity of permeability in the reservoir, any flow related data such as pw(u't) is particularly valuable for generating accurate reservoir models. Then conditioning to the well-specific TFw-response such as defined in (1) would result in permeability models k(u); u ? Reservoir which would predict more accurately the future reservoir performances corresponding to different transfer functions. The permeability fields k(u) have to be such that they honor well data at their locations and conform to a prior model of spatial variability, for example to a variogram model ?(h) that measures the spatial variability between pairs of locations. In addition the well pressure is honored in that:where TFw is the transfer function (flow simulator) modeling the well response. Keywords: correlation 0, proxy, prediction, reservoir simulation, multiple point average, artificial intelligence, realization, permeability field, template, calibration Subjects: Reservoir Fluid Dynamics, Reservoir Simulation, Formation Evaluation & Management, Flow in porous media, Evaluation of uncertainties, Drillstem/well testing This content is only available via PDF. 2000. Society of Petroleum Engineers You can access this article if you purchase or spend a download.

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