Geological Characterization Of Naturally Fractured Reservoirs Using Multiple Point Geostatistics
Bibliographic record
Abstract
Geological Characterization Of Naturally Fractured Reservoirs Using Multiple Point Geostatistics Xiaohuan Liu; Xiaohuan Liu Univ. of Calgary Search for other works by this author on: This Site Google Scholar Sanjay Srinivasan; Sanjay Srinivasan Univ. of Calgary Search for other works by this author on: This Site Google Scholar Dale Wong Dale Wong Object Reservoirs Inc. Search for other works by this author on: This Site Google Scholar Paper presented at the SPE/DOE Improved Oil Recovery Symposium, Tulsa, Oklahoma, April 2002. Paper Number: SPE-75246-MS https://doi.org/10.2118/75246-MS Published: April 13 2002 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Liu, Xiaohuan, Srinivasan, Sanjay, and Dale Wong. "Geological Characterization Of Naturally Fractured Reservoirs Using Multiple Point Geostatistics." Paper presented at the SPE/DOE Improved Oil Recovery Symposium, Tulsa, Oklahoma, April 2002. doi: https://doi.org/10.2118/75246-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 Improved Oil Recovery Conference Search Advanced Search AbstractThe spatial distribution of fractures in a reservoir affects the displacement of fluids and the prediction of future performance. Realistic characterization of fractured reservoirs requires quantification and classification of fracture patterns on the basis of the underlying geological characteristics and developing reservoir modeling algorithms that can integrate connectivity based (multiple point) statistics related to fracture patterns. A methodology for summarizing the characteristics of fracture networks based on multiple point connectivity functions is presented. The paper also presents a stochastic simulation methodology for constraining the target reservoir model to the connectivity characteristics derived from analog models and to all other available reservoir specific data in the form of well information and seismic areal proportion maps.IntroductionA natural fracture is a planar discontinuity in reservoir rock due to deformation or physical diagenesis1. Fractures may have either a positive or negative effect on fluid flow depending on whether they are open or sealed due to mineralization. For the purposes of this paper, a fractured reservoir is defined as a reservoir in which naturally occurring fractures are predicted to have a significant effect on fluid flow either in the form of increased permeability and/or porosity or increased permeability anisotropy.Natural fracture patterns are frequently interpreted on the basis of laboratory-derived fracture patterns corresponding to models of paleo-stress fields and strain distribution in the reservoir at the time of fracture2. Stearns and Friedman3 proposed a genetic classification of fracture systems based on stress/strain conditions in laboratory samples and features observed in outcrops and sub-surface settings. Based on their work, fractures are generically classified into:Shear Fractures - exhibit a sense of displacement parallel to the fracture plane. Shear fractures form when the stresses in the three principal directions are all compressive. They form at an acute angle to the maximum principal stress direction and at an obtuse angle to the minimum compressive stress direction.Extension Fractures - exhibit a sense of displacement perpendicular to and away from the fracture plane. They form perpendicular to the minimum stress direction. They too result when the stresses in the three principal directions are compressive and can occur in conjunction with shear fractures.Tension Fractures - Exhibit a sense of displacement perpendicular to and away from the fracture plane. However, in order to form a tension fracture, at least one of the principal stresses has to be tensile. Since rocks exhibit significantly reduced strength in tension tests, the frequency of fractures under tensile stress conditions is more. Keywords: proportion, probability, different direction, spe 75246, node, algorithm, bayesian inference, fracture, configuration, geologic modeling Subjects: Hydraulic Fracturing, Reservoir Characterization, Unconventional and Complex Reservoirs, Faults and fracture characterization, Geologic modeling, Naturally-fractured reservoirs This content is only available via PDF. 2002. 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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".