Streamline-based Production Data Integration Under Changing Field Conditions (2001)

Streamline-based Production Data Integration Under Changing Field Conditions Zhong He; Zhong He 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 Seongsik Yoon Seongsik Yoon Texas A & M University Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, September 2001. Paper Number: SPE-71333-MS https://doi.org/10.2118/71333-MS Published: September 30 2001 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation He, Zhong, Datta-Gupta, Akhil, and Seongsik Yoon. "Streamline-based Production Data Integration Under Changing Field Conditions." Paper presented at the SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, September 2001. doi: https://doi.org/10.2118/71333-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu 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 AbstractRecently streamline-based flow simulation models have offered significant potential in integrating dynamic data into high-resolution reservoir models. A unique feature of the streamline-based production data integration has been the concept of a 'travel time' match that is analogous to seismic tomography and has allowed the use of efficient and proven techniques from geophysics. In this paper we propose a 'generalized travel time' inversion method for production data integration that is particularly well-suited for large-scale field applications with changing conditions. Instead of matching the production data directly, we minimize a 'travel time shift' derived by maximizing a cross-correlation between the observed and computed production response at each well. There are several advantages with our proposed method. First, it is general and extremely computationally efficient. The travel time sensitivities can be computed analytically using a single streamline simulation that can be orders of magnitude faster than a conventional reservoir simulator. Second, it is very robust and the minimization is relatively insensitive to the choice of the initial model. Finally, it is field-proven because we utilize efficient techniques from geophysical inverse theory.We demonstrate the power and utility of our proposed method using synthetic and field examples. The synthetic examples include a large-scale 3D example with quarter-million grid cells involving infill drilling and pattern conversions. The field example is from the Goldsmith San Andres Unit (GSAU) in West Texas and includes multiple patterns with 11 injectors and 31 producers. Starting with a reservoir model based on well log and seismic data, we integrate water-cut history for 20 years in less than 2 hours in a PC.IntroductionIt is well known that geological models derived from static data only, such as geological, well log, core and seismic data, often fail to reproduce the production history. Reconciling geologic models to the dynamic response of the reservoir is critical to building reliable reservoir models. Past few years, have seen significant developments in the area of such dynamic data integration through the use of inverse modeling.1–13 Streamline models have shown great promise in this regard.9–13 The key advantages of streamline-based production data integration are its computational efficiency as a "forward" model and analytic computations of sensitivities of the production response with respect to reservoir parameters using a single flow simulation.9–11 Sensitivities describe the change in production response because of a small perturbation in reservoir properties such as porosity and permeability and are a vital part of the dynamic data integration process.Our previous works on streamline-based production data integration followed directly from seismic waveform inversion and utilized a two-step procedure9,10 (i) a travel time match that involves matching of the 'first arrival' or breakthrough times and (ii) an amplitude match involving matching of the actual production response. The two-step approach has been shown to substantially speed-up the computation and also prevents the solutions from being trapped by secondary peaks in the production response. However, a majority of the production data misfit reduction occurs during the travel time inversion and most of the large-scale features of heterogeneity are resolved at this stage.9,10 Keywords: artificial intelligence, pressure update, sensitivity, field condition, misfit, generalized travel time, reservoir simulation, travel time inversion, reservoir characterization, production response Subjects: Reservoir Characterization, Reservoir Simulation, Seismic processing and interpretation This content is only available via PDF. 2001. Society of Petroleum Engineers You can access this article if you purchase or spend a download.