Optimization of Well Placement and/or Borehole Trajectory for Minimum Drilling Cost (A Critical Review of Field Case Studies)
Bibliographic record
Abstract
Abstract This paper provides a brief explanation of the factors that affect optimal well placement and/or borehole trajectory and how these factors can be controlled or selected to give optimized well trajectories. Review of some of the general techniques used in achieving optimal well placement and/or borehole trajectory, with a view to minimizing drilling cost, are presented and some field case studies/field examples selected from literature are given. Limitations of some of these techniques are also discussed. Introduction Drilling of wells for optimal placement within the reservoir or target is one of the most important challenges in modern day drilling problems. The cost associated with drilling operations can run into several millions of dollars in land, swamp and offshore environments. With the advent of deep-offshore drilling, with its associated higher cost, the need for optimal well placement cannot be over-emphasized. Good drilling practices and planning, that bring about accurate or near accurate well placement or accurate borehole trajectory tracking can significantly reduce drilling cost by eliminating the need to plan or drill additional wells. Several factors come into play during drilling that can affect optimal well placement or well trajectory. Many of these factors can be controlled easily while others are particularly difficult to control. Also, a previous knowledge of the drilling environment, in terms of formation type, BHA behaviour and hole stability, can be helpful in making better planning for optimal well placement. Previous knowledge can be used to simulate well trajectory torque, BHA frictional resistance, expected build or turning rate, etc. Planning for a directional or horizontal well encompasses hitting the target point(s) with precision and accuracy at low cost. Factors to be considered include; well total depth, target inclination and direction, curvature and turning sections, and kick-off points. Other issues include the type of tools used in monitoring and the error associated with such tools, method of well planning and how the well is actually drilled. The purpose of this paper is to briefly discuss factors that affect optimal well placement and/or borehole trajectory and review several techniques, available in the literature for optimizing well placement and/or borehole trajectory. Some relevant field case studies/field examples are also discussed in the paper. Factors that Affect Well Placement Many factors that affect bore hole trajectory and accurate well placements are directly related to the factor affecting hole angle and inclinations. These include the type of bottom hole assembly (BHA) in terms of size and elastic properties; borehole shape and curvature; bit-type; formation strength and anisotropy; and drilling parameters and conditions (1). BHA Type The stiffness property of BHA components plays a major role in critical well placement. The stiffness property is dependent on the modulus of elasticity and the moment of inertial of the BHA components. BHA components should be selected in such a way that the stiffness property can withstand borehole conditions for effective bore control.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".