Integration of Pressure and Rate Transient Analysis for Transverse and Longitudinal Multistage Fractured Horizontal First Unconventional Gas Well in Egypt
Bibliographic record
Abstract
Abstract Recent advancements in technologies pertaining to the drilling of horizontal well and multistage hydraulic fracturing have made it possible to get significant hydrocarbon production even from extremely low permeability formations. Evaluation of the economics is becoming increasingly essential before committing to any big investment. In this scenario, production forecasting plays an important role by not only evaluating the economic feasibility of the project, but also helping in the selection of the most optimal development strategy (Shivam et al. 2017). This paper presents an integrated workflow that has been applied in one of unconventional gas bearing formation which is a common reservoir in Egypt’s Western Desert. It does form a reasonable reservoir size and spread over several hundred sq.kms. It is characterized as a low permeability carbonate (0.2 mD) in soft chalk reservoir. Many vertical wells were drilled and completed in the appraisal program for collecting the required data to evaluate reservoir performance before the completion of horizontals, but economical target production rates could not be achieved. To help optimize field development strategy and further increase production, a full field development plan was initiated by drilling horizontal wells with multistage fracturing stimulation. Horizontal pilot wells were drilled and completed along and perpendicular the minimum horizontal in-situ stress direction to enable both transverse and longitudinal fracture propagation patterns for the best completion option. The objective of this paper is to present an integrated approach to evaluate an unconventional resource, improve the completion efficiency, improving the future fracture design and understand the productivity enhancement specifically in the Western Desert of Egypt, through a detailed analysis of production data and pressure transient analysis. The conclusions from this study will help in evaluating the behavior of multistage fractured horizontal with different fracture azimuth direction and generate production forecast for different development scenarios. The stimulated rock volume estimation will help in planning the future fracture design to increase well EUR. The proposed workflow and lessons learned formed the basis for subsequent development of various unconventional plays in Egypt.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".