Iterative Integration of Dynamic Data in Reservoir Models
Bibliographic record
Abstract
Iterative Integration of Dynamic Data in Reservoir Models Tarun Kashib; Tarun Kashib U. of Calgary Search for other works by this author on: This Site Google Scholar Sanjay Srinivasan Sanjay Srinivasan U. of Texas at Austin Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, October 2003. Paper Number: SPE-84592-MS https://doi.org/10.2118/84592-MS Published: October 05 2003 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Kashib, Tarun, and Sanjay Srinivasan. "Iterative Integration of Dynamic Data in Reservoir Models." Paper presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, October 2003. doi: https://doi.org/10.2118/84592-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 Annual Technical Conference and Exhibition Search Advanced Search AbstractConditioning reservoir models to dynamic data such as historical production response is challenging because of the complexity of the relationship between the model parameters and the response variable. The focus of this paper is to present a methodology for efficiently integrating dynamic production data into reservoir models. In contrast to some of the other methods, the proposed methodology attempts to quantify the information in production data pertaining to reservoir heterogeneity in a probabilistic manner. The conditional probability representing the uncertainty in permeability at a location is iteratively updated to account for the additional information contained in the dynamic response data. A localized perturbation procedure is also presented to account for multiple flow regions within the reservoir. Such an improved scheme utilizes a set of locally varying deformation parameters to guide the iterative updating process in order to obtain a global history match.IntroductionGeostatistics provides a framework for integrating diverse types of reservoir specific data in order to develop multiple realizations of the reservoir. The acquired data may display spatial dependency only (static data) such as well logs, core measurements, etc. or may display joint space-time dependency (dynamic data) such as production response, time-lapse seismic, etc. Several algorithms are available to condition reservoir models to static data, however conditioning to dynamic data is complex because of the non-linear relationship between the input model parameters (spatially varying petrophysical properties) and the output response (e.g. well pressure as a function of time). Manually adjusting reservoir models so that they reflect the dynamic response information accurately is very time consuming and tedious. In addition, such manual adjustments might lead to reservoir models that do not exhibit the correct spatial covariance structure. Consequently, though the adjusted reservoir models may display an excellent match to the historical production records, they may yield totally erroneous future predictions of reservoir performance. The ability to forecast future production scenarios accurately is the ultimate objective of any reservoir modeling exercise.This problem could be alleviated if the historical dynamic data are integrated into the reservoir model construction step such that the final model is conditioned to all the available static data as well as the dynamic data. Provided the rules for integrating production information into geologic models can be clearly established through calibration, incremental information derived from production data collected during the productive life of the field can be used to continuously update reservoir models. Keywords: integration, history matching, permeability field, dynamic data, reservoir simulation, probability, realization, reservoir model, spe 84592, information Subjects: Reservoir Simulation, Evaluation of uncertainties, History matching This content is only available via PDF. 2003. Society of Petroleum Engineers You can access this article if you purchase or spend a download.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".