Full-Field Simulation Supports Reservoir Management Decisions in a Giant Carbonate Complex
Bibliographic record
Abstract
Abstract The Ku, Maloob and Zaap (KMZ) complex is located offshore in the shallow waters of the Gulf of Mexico, in the bay of Campeche, 105 kilometers from Ciudad del Carmen. The complex oil production averaged 850, 000 BOPD in 2012. KMZ has been Mexico's largest contributor to production since January 2009. The fluid is heavy oil of 22°API in Ku, and 13°API in Maloob-Zaap. The Cretaceous is the main producing formation. This carbonate reservoir is a naturally fractured dolomite that also contains matrix and vuggy porosity. Average permeability and gross thickness are 4, 000 md and 700 m, respectively. Field production began in 1981. More than 150 wells were active in 2012. The initial reservoir pressure of 4, 594 psi declined to about 1, 707 psi as of 2012. Nitrogen injection began in 2009 for pressure maintenance. Reservoir simulation has been used in the KMZ complex during the last 15 years to support key reservoir management decisions. The Cretaceous reservoir was simulated as a dual porosity/single permeability system. The different fluids of Ku and Maloob-Zaap were each represented by a 6-component equation of state. The simulation was implemented as compositional to model the nitrogen injection. The aquifer volume was represented by applying pore volume multipliers to grid cells. One set of gas-oil and water-oil relative permeability and capillary pressure was assigned to the whole reservoir, along with one pressure dependent pore volume compressibility. Simple input parameters were used whenever possible for the history match. The simulation model matched the historical pressure and the production of oil, water, and gas by field satisfactorily. The match by well was fit-for-purpose. The production/injection forecasts helped support reserves, the complex plateau rate, the pressure maintenance strategy, the development scenario, and the timing and capacity of future facilities needed to manage increasing water production. This case study shows that even though reservoir simulation has limitations as any other tool, it is excellent for validating reservoir mechanisms and serving as the basis for estimating field long-term production performance, and providing support for economic and financial decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".