Rapid History Matching Using a Generalized Travel Time Inversion Method
Zhan Wu, Akhil Datta‐Gupta · 2001
Rapid History Matching Using a Generalized Travel Time Inversion Method Zhan Wu; Zhan Wu Texas A&M University Search for other works by this author on: This Site Google Scholar Akhil Datta-Gupta Akhil Datta-Gupta Texas A&M University Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Reservoir Simulation Symposium, Houston, Texas, February 2001. Paper Number: SPE-66352-MS https://doi.org/10.2118/66352-MS Published: February 11 2001 Cite View This Citation Add to Citation Manager Share Icon Share MailTo Twitter LinkedIn Get Permissions Search Site Citation Wu, Zhan, and Akhil Datta-Gupta. "Rapid History Matching Using a Generalized Travel Time Inversion Method." Paper presented at the SPE Reservoir Simulation Symposium, Houston, Texas, February 2001. doi: https://doi.org/10.2118/66352-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 Reservoir Simulation Conference Search Advanced Search Abstract We propose a generalized travel time inversion method for production data integration into reservoir models using finite-difference reservoir simulators. Our approach is motivated by seismic waveform imaging and is particularly well-suited for large-scale field applications because the computation cost depends only on the number of wells regardless of the number of parameters or the amount of observed data. Instead of matching the production data directly, we minimize a 'travel time shift' at each well derived by maximizing the cross-correlation between the observed and calculated production response. An optimal control method is used to compute the sensitivity of the travel time with respect to reservoir parameters. Finally, data integration is carried out via a modified Gauss-Newton method.There are several advantages associated with the proposed travel time inversion method. First, it is robust and computationally efficient. The travel time misfit function is quasilinear with respect to changes in reservoir properties. As a result, the minimization is relatively insensitive to the choice of the prior model. Second, the computational cost associated with the sensitivity computation depends only on the number of wells which can be orders of magnitude lower than the number of parameters or the amount of observed data. This offers tremendous advantage over the commonly used gradient simulator method or the conventional adjoint methods that attempt to minimize the production data directly. Furthermore, the travel time approach also offers computational advantage during minimization of the misfit function using the Gauss-Newton algorithm. We have presented several examples to demonstrate the power, generality and practical feasibility of our proposed approach for large-scale field applications. Keywords: production data, optimal control method, artificial intelligence, reservoir model, geologic modeling, reservoir simulation, upstream oil & gas, spe 66352, reservoir characterization, production response Subjects: Reservoir Characterization, Improved and Enhanced Recovery, Reservoir Simulation, Formation Evaluation & Management, Information Management and Systems, Geologic modeling, History matching Copyright 2001, Society of Petroleum Engineers You can access this article if you purchase or spend a download.