Geomechanical Quantitative Evaluation Method for Connectivity in Ultra-Deep Fault-Controlled Carbonate Reservoirs
Bibliographic record
Abstract
Abstract In the Tarim Basin's ultra-deep fault-controlled carbonate reservoirs, western China, development faces two primary challenges: significant inter-well productivity variations and insufficient data for optimal well placement and trajectory design. Additionally, while initial production in these reservoirs is high, sustained output is limited, exacerbated by lacking of systematic research on reservoir connectivity, crucial for effective water flooding strategies. Evaluating connectivity in drilling-constrained environments remains a significant hurdle, affecting well placement and flooding efficiency in such reservoirs. This study presents a novel connectivity discriminator, Factor of Reservoir Connectivity (FRC), for fractured carbonate reservoirs. Utilizing 3D fracture network analysis, a regional geomechanical model is constructed. Stress tensor transformations reveal the stress states of fracture cells, and critical stresses are calculated using the principle of critical stress fracturing. This determines the critical opening pressure, establishing a discriminant index for fracture mechanical activity. Furthermore, a quantitative method assesses connectivity in fault-controlled carbonate reservoirs by integrating the fracture activity index with seismic interpretation attributes, normalized with weights to create a reservoir connectivity discriminant factor. A comprehensive 3D geomechanical model, a 3D fracture mechanics activity distribution model, and a 3D reservoir connectivity factor distribution model were established for the Fuman Oilfield in the northern Tarim Basin. These models delineate connectivity across diverse fault zones, varying lateral positions within the same fault zone, and distinct vertical depths. By integrating geological findings, the study investigated hydrocarbon charging and accumulation, prioritizing well locations based on favorable fracture activity within the same connectivity unit. Well trajectory optimization considered varying depths to control the entire hydrocarbon reservoir. This research provided a more scientific and quantitative basis for well location deployment and trajectory optimization in key fracture zones like FⅠ17 and FⅠ19, and for the quantitative design of water flooding schemes in the Fuyuan 210 fracture zone. This approach enhanced the proportion of high-yield wells from 35% to 70%, with cumulative incremental oil production exceeding 1.2 million tons, supporting sustained high and stable production in the Fuman Oilfield The connectivity of fault-controlled reservoirs is governed by natural fracturing under certain stresses, leading to tensile opening or shear displacement. This method computes connectivity using mechanical and seismic attributes with limited drilling data. This method calculates connectivity factors for entire 3D work areas with multiple faults, and scalable in large depth ranges, delineating distinct reservoir connectivity units. The successful implementation offers a valuable reference for the quantitative evaluation of connectivity in similar carbonate reservoirs globally.
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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 | 0.001 | 0.001 |
| 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".