Geological Characteristics and Integrated Development Plan for Giant Naturally Fractured Basement Reservoirs
Bibliographic record
Abstract
Abstract A great portion of the world’s oil reserves is contained in naturally fractured reservoirs. As the conventional oil and gas reservoirs have become significantly depleted whereas energy demand is sharply increases, NFRs play an important role in oil exploration and makes a large contribution toward oil and gas production worldwide. However, characterization of fractured reservoir is very complex as compared as conventional reservoirs. Lacking of experiences during production stages may quickly destroy entire reservoir. Therefore, the successful case studies as well as failure lessons should be highlighted for improving recovery efficiency in such complex reservoirs. This paper aims to introduce two historical case studies of successful development plan for giant fractured granite basement reservoirs in Viet Nam. These reservoirs contain huge hydrocarbon resources in basement source rock and present a unique geological characterization, very high heterogeneity, high temperature and closure stress. A detailed geological understanding of the reservoir, along with a creative reservoir simulation, is needed to determine the optimal recovery method for these reservoirs. These are the keys to having a successful operation, as well as reducing uncertainties and achieving the most efficient of oilfield management. With a large database collected from over twenty years of production period of over 215 wells, the authors developed a workflow for integrating between static and dynamic data. The geological characterization of two typical basement reservoirs was thoroughly analyzed to figure out their effect on recovery schemes. A new approach for building geological model by artificial neuron network technique was introduced, and these integrated results have been served as input data for simulation with IMEX in CMG simulator. Based on the reservoir modeling, we proposed a promising method for improving oil recovery factor by optimizing the well network locations, application of horizontal well and gaslift. From our historical experiences, the authors introduce the most appropriate method for overcoming the challenge of waterflooding operation and stimulation for fractured basement reservoirs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".