A Successful Story Of An Integrated Geologic And Reservoir Engineering Approach Of The Gandu Unit
Bibliographic record
Abstract
Abstract This paper presents the success story of an integrated approach to optimize the production performance of the Goldsmith Andector Unit (Gandu) in West Texas. All production is from the Clearfork formation, a typical carbonate reservoir characterized by large and discontinuous pay intervals with low reservoir energy and high residual oil saturation. Re-development of this mature field began as a 20-acre infill drilling program in 2001 and has been under waterflood expansion since 2008. A multi-disciplinary team was commissioned to improve production in Gandu. The team used an aggressive approach towards development practices of all aspects including reservoir engineering, geologic, and operational practices. Reservoir characterization and numerical simulation work in conjunction with classical methods validated the 650 MMSTB of original oil in place (OOIP) and the 64 MMSTB estimated waterflood reserves in the reservoir. The team focused on optimizing the base production, monitoring well performance, and identifying opportunities to increase production through workovers, returning-to-production (RTP) jobs and recompletions. This paper details the systematic approach that was followed in order to achieve waterflood expansion success including geological characterization, reservoir engineering, data acquisition, production monitoring, well automation, field optimization, and program development for subsequent years. Details of the workflow implemented under the technical approach, best operational practices, and lessons learned are discussed. As a result, the production of the field increased approximately 70%, with a total increase of 2,800 BOEPD by 2009. The field continues to produce significantly more than it did prior to the waterflood expansion in 2008.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".