Optimization of Horizontal Wellbore Trajectory and Placement of Hydraulic Fracturing Stages in Tight Heterogeneous Gas Condensate Reservoirs Using Derivative Free Algorithms
Bibliographic record
Abstract
Abstract We present hybrid derivative free algorithm methods that maximize NPV while solving the problem of placement of hydraulic fracturing stages and horizontal wellbore trajectory in a tight heterogeneous gas condensate reservoir. These parameters are important in reservoir management and field development optimization as they determine the interaction of the wellbore with the reservoir. Thus, they have a large impact on production performance of the field and the cost of field development. We couple a tight heterogeneous gas condensate reservoir model to an optimization algorithm that determines the parameters that maximize the economic value of the field. The optimization algorithm proposed in this paper exploits the advantage of stochastic global search (particle swarm optimization) and local pattern search (hill climber) techniques to find the optimized parameters. The optimization process starts with the particle swarm optimization (PSO) algorithm, which is executed with an initial guess based on engineering experience until the objective function (in this study NPV) fails to improve in the next couple of iterations. This is followed by the hill climber (HC) algorithm that improves the objective function. Observations from our investigation show that an optimal field development plan (FDP) is essential to optimize the placement of hydraulic fracturing stages along a horizontal wellbore and to optimize the wellbore trajectory inside the reservoir. However, these parameters would have to be optimized simultaneously in a non-systematic manner. We integrate reservoir engineering experience and economics knowledge into the optimization algorithm by embedding practical constraints into the problem formulation. The algorithm is executed in a reasonable amount of computation time, considering the complexity of the problem. The optimization process evaluates various possible field development plans involving different hydraulic fracturing stages and well placement trajectories. The investigation demonstrates how these parameters impact the economic value of the field, how to optimize the placement of hydraulic fracture stages along a horizontal wellbore, and how to optimize the wellbore trajectory inside the reservoir. The methodology presented in this work should allow industry professionals working with unconventional reservoirs to improve the economic value of a field in a shorter timeframe while considering all possible field development plans, a task that would be time consuming and tedious if carried out manually.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".