Coupled Reservoir-Geomechanics Model With Sand Erosion for Sand Rate and Enhanced Production Prediction
Bibliographic record
Abstract
Coupled Reservoir-Geomechanics Model With Sand Erosion for Sand Rate and Enhanced Production Prediction Yarlong Wang; Yarlong Wang Petro-Geotech Inc. Search for other works by this author on: This Site Google Scholar Shifeng Xue Shifeng Xue Petro-Geotech Inc. Search for other works by this author on: This Site Google Scholar Paper presented at the International Symposium and Exhibition on Formation Damage Control, Lafayette, Louisiana, February 2002. Paper Number: SPE-73738-MS https://doi.org/10.2118/73738-MS Published: February 20 2002 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Wang, Yarlong, and Shifeng Xue. "Coupled Reservoir-Geomechanics Model With Sand Erosion for Sand Rate and Enhanced Production Prediction." Paper presented at the International Symposium and Exhibition on Formation Damage Control, Lafayette, Louisiana, February 2002. doi: https://doi.org/10.2118/73738-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search nav search search input Search input auto suggest search filter All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE International Conference and Exhibition on Formation Damage Control Search Advanced Search AbstractA fully coupled reservoir-geomechanics model is developed to simulate the enhanced production phenomena both in heavy-oil reservoirs (i.e. Northwestern Canada) and conventional oil reservoirs (i.e. North Sea). The model is implemented numerically by fully coupling an extended geomechanics model to a two-phase reservoir flow model. A sand erosion model is postulated after the onset of sand production, which is determined based on the degree of plastic deformation inside the reservoir formation calculated by the coupled reservoir-geomechanics model. Both the enhanced production and the ranges of the enhanced or sanding zone are calculated, the effect of solid production on oil recovery and enhancement are analyzed. Field data for solid production and enhanced oil production from Frog Lake (Lloydminster, Canada) are used to validate the model for the sand rate and sand production. Our studies indicate that the enhanced oil production can be contributed by eitherthe a large-scale reservoir formation mobility improvement, (i.e. wormhole type model), bya higher fluid velocity due to the movement of the sand particles according to the modified Darcy's flow, or byan effective well radius increase or negative skin development due to sand erosion if formation does not permit an extensive erosional zone. Such an improvement on productivity reduces the near well pressure gradient so that the sanding potential is weakened, but permits an easier path for oil to flow into the well due to an enhanced permeability. Two-phase flow can affect pressure gradient and formation residual cohesion due to capillary pressure buildup. Indirectly, production enhancement strategy can be controlled by the water saturation distribution and development, as the success and economic value of a field operation can depend on if sand production can be induced or not. Such an analogy can also be used for a completion strategy by allowing a certain amount of sand production before gravel pack in high flow-rate reservoir.IntroductionSand production is a phenomenon that occurs during aggressive production induced by the in-situ stress concentration near a wellbore and perforation tips in poorly cemented formations. Such a solid production compromises oil production, increases completion costs, and reduces the life cycles of equipment down hole and on the surface. Sand production has been a major concern to production engineers for decades, either in poorly consolidated reservoirs or from those formation with cement. These sanding effects often are associated with high production rates, and the issue is becoming more critical these days as operators are following more aggressive production schedules. Sand production, on the other hand, has been proven a most effective way to increase well productivity both in heavy oil and light oil reservoirs1,7. A typical 4–10 fold increase in oil production is normal in heavy oil reservoirs (Cold Production)1,6, and up to a 44% increase in sand-free rate after a certain amount of sand production in conventional oil reservoirs has been reported8,9. For conventional oil producers, both enhanced production and improved sand-free rate are highly desirable. Whereas for the heavy crude operators, other than the improved productivity, operating cost reduction is vital for a profitable operation, because the price margin between heavy oil and light oil is high (this is particularly important for cold production operators in northwestern Canada). The costs associated with such an operation are usually a result of high work-over frequency, short PC-pump life, sand disposal vs. potential enhancement, in-filled drilling costs, pump down-time, and production decline after well shut-in, etc. In attempt to maximize oil production and minimize costs during cold production, operators depend on experience and empirical models to evaluate cold production performance because of the complex nature of the fluid/solid slurry flow processes involved. A quantitative model will allow producers to understand this unique production process, evaluate the impact of sand production on reservoir enhancement, and provide an efficient tool to reduce unnecessary costs during the field operations. Keywords: reservoir characterization, deformation, reservoir geomechanics, relative permeability, upstream oil & gas, reservoir simulation, prediction, sand erosion, erosion, saturation Subjects: Reservoir Characterization, Reservoir Fluid Dynamics, Reservoir Simulation, Formation Evaluation & Management, Reservoir geomechanics, Flow in porous media 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 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".