Streamline-Based Production-Data Integration into High-Resolution Reservoir Models

Datta‐Gupta, Akhil; Yoon, Sungroh; Barman, Indranil; Vasco, D. W.

doi:10.2118/1298-0072-jpt

Cited by 28 publications

(21 citation statements)

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“…[1][2][3][4][5][6][7][8][9][10] The production data misfit is commonly represented as follows: ) for i‫,1ס‬иии, N w , j‫,1ס‬иии, N d .…”

Section: Travel Time and Sensitivity Coefficients Of Travel Timementioning

confidence: 99%

“…solution robustness and rapid convergence during minimization. [8][9][10][13][14][15] Second, the entire production history misfit at a well is essentially reduced to a single datum representing the optimal travel-time shift. This latter feature assumes great significance in improving the computational efficiency associated with the automatic history matching procedure as discussed in the next section.…”

Section: Travel Time and Sensitivity Coefficients Of Travel Timementioning

confidence: 99%

“…[1][2][3][4][5][6][7][8][9][10] These techniques allow engineers to build reservoir models that honor field production history and static information such as well logs, core, and seismic data. The theoretical basis of these techniques is generally rooted in the least-squares inversion theory.…”

Section: Introductionmentioning

confidence: 99%

“…[8][9][10][11][12] 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. Instead of matching the production data directly, the observed data and model predictions are first lined up at the breakthrough time.…”

Section: Introductionmentioning

confidence: 99%

“…A major part of the production data misfit reduction occurs during the travel-time inversion, and most large-scale features of heterogeneity are resolved at this stage. [8][9][10] There are several advantages associated with a travel-time inversion of production data. First, it is robust and computationally efficient.…”

Section: Introductionmentioning

confidence: 99%

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Rapid History Matching Using a Generalized Travel-Time Inversion Method

Datta‐Gupta

2002

SPE Journal

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We propose a generalized travel-time inversion method for production data integration into reservoir models using finitedifference-based 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 proceeds rapidly even if the prior model is not close to the solution. 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 a 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. IntroductionIn recent years, several techniques have been developed for integrating production data into reservoir models. 1-10 These techniques allow engineers to build reservoir models that honor field production history and static information such as well logs, core, and seismic data. The theoretical basis of these techniques is generally rooted in the least-squares inversion theory. It is well known that inverse problems are typically ill-posed and can result in nonunique and unstable solutions. Proper incorporation of static data in the form of a prior model can partially alleviate the problem. However, there are additional outstanding challenges that have deterred the routine integration of production data into reservoir models. First, the computational cost is still extremely high, particularly when conventional finite-difference reservoir simulators are used for "forward" modeling. Under such conditions, most of the current methods become computationally prohibitive when a large number of parameters and observed data are involved. Second, the relationship between the production response and reservoir properties can be highly nonlinear. This often causes the solution to converge to a local minimum. ...

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“…[1][2][3][4][5][6][7][8][9][10] The production data misfit is commonly represented as follows: ) for i‫,1ס‬иии, N w , j‫,1ס‬иии, N d .…”

Section: Travel Time and Sensitivity Coefficients Of Travel Timementioning

confidence: 99%