Reservoir Modeling for Horizontal Exploitation of a Giant Heavy Oil Field - Challenges and Lessons Learned
Bibliographic record
Abstract
Reservoir Modeling for Horizontal Exploitation of a Giant Heavy Oil Field - Challenges and Lessons Learned T. H. Tankersley; T. H. Tankersley Petrolera Ameriven Search for other works by this author on: This Site Google Scholar M.W. Waite M.W. Waite Petrolera Ameriven Search for other works by this author on: This Site Google Scholar Paper presented at the SPE International Thermal Operations and Heavy Oil Symposium and International Horizontal Well Technology Conference, Calgary, Alberta, Canada, November 2002. Paper Number: SPE-78957-MS https://doi.org/10.2118/78957-MS Published: November 04 2002 Connected Content Related to: Reservoir Modeling for Horizontal Exploitation of a Giant Heavy-Oil Field Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Tankersley, T. H., and M.W. Waite. "Reservoir Modeling for Horizontal Exploitation of a Giant Heavy Oil Field - Challenges and Lessons Learned." Paper presented at the SPE International Thermal Operations and Heavy Oil Symposium and International Horizontal Well Technology Conference, Calgary, Alberta, Canada, November 2002. doi: https://doi.org/10.2118/78957-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 International Thermal Operations and Heavy Oil Symposium Search Advanced Search AbstractThe Hamaca Field, located in Venezuela's Orinoco Heavy Oil Belt, is a giant extra-heavy oil accumulation operated by Ameriven, an operating agent company for PDVSA, Phillips and ChevronTexaco. Over the 35-year life of the field, more than one thousand horizontal laterals are planned in order to deliver 190,000 BOPD to a heavy-oil upgrader facility. Reservoir models are built to support a broad continuum of activities in order to meet this objective. This paper will review the Hamaca reservoir modeling process, the challenge of integrating many sources of geologic and geophysical constraints including horizontal well information, the focus on continuous model improvement, and issues unique to Hamaca rock and fluid properties.BackgroundThe Hamaca Field is located in Venezuela's Orinoco Heavy Oil Belt, which is reported to contain more than 1.2 trillion barrels of heavy and extra heavy oil in a huge stratigraphic trap on the southern flank of the Oriente Basin (Fig. 1). The Hamaca concession area, which covers 160,000 acres, contains 8–10 API gravity oil trapped in shallow fluvial-deltaic reservoirs of the Oficina Formation (Miocene age). Sandstone reservoirs of the Oficina Formation at Hamaca were generally deposited in a bed-load dominated, fluvial-deltaic environment. Reservoir properties are excellent with porosity values of up to 36% and permeability values of up to 30 darcies. Hamaca crude is considered "foamy" and is generally saturated with gas at reservoir conditions1.Over the 35-year life of the field, over 1000 horizontal laterals are planned in order to deliver 190,000 BOPD to a heavy-oil upgrader facility, which is currently under construction1. To date, more than 110 horizontal wells have been drilled to produce from the Hamaca reservoirs. Oil is being produced under "cold production" methods, (no added heat) using progressive cavity pumps to bring oil to the surface. Cold production is possible due to the extended length of the horizontal wells (5000'), excellent reservoir properties and the "foamy oil" nature of Hamaca crude2. The heavy oil will be mixed with diluent just downstream of the wellheads to facilitate transport to the upgrader facility. The Hamaca crude will be converted to a sweeter crude product of approximately 26° API at the upgrader.The combined use of both well and seismic data is critically important for characterizing the stratigraphic complexity of the Hamaca fluvial-deltaic systems. To assist in targeting sweet spots for horizontal well placement, a 250 km2 3-D seismic survey was acquired along with the drilling of 91 stratigraphic information wells with an average separation distance of about 1.5 km. Keywords: variability, property pdf, variogram, upstream oil & gas, modeling & simulation, heterogeneity, gradient, reservoir characterization, artificial intelligence, permeability Subjects: Reservoir Characterization, Geologic modeling This content is only available via PDF. 2002. SPE/PS-CIM/CHOA International Thermal Operations and Heavy Oil Symposium and International Horizontal Well Technology Conference 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 | 0.000 |
| 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".