Record RSS One Run High DLS Curve and Three Mile Lateral in the Delaware Basin: A Permian Case Study
Bibliographic record
Abstract
Abstract Operators in the Permian Basin are drilling longer lateral sections to maximize reservoir exposure while improving drilling efficiencies and reducing surface footprint. The economics of drilling a larger 8.5″ open hole curve section and three-mile lateral is challenging to drill with less than two bottom hole assemblies (BHA). Drilling a high DLS curve with 6.75″ tools while still utilizing the same BHA to drill a three-mile lateral requires systematic planning and execution to achieve greater degree of consistency. Multiple challenges are associated with well trajectory design, downhole vibration mitigation, drilling practices, BHA optimization, and matching the system with an optimized drill bit design. The mud motor that drives RSS BHA bypasses a significant flow pumped through the BHA to cool the motor bearing stack. This additional constraint can be challenging to optimize hydraulic horsepower at the bit and RSS steering performance to drill a high DLS curve while achieving drilling performance in the lateral section. The paper describes the methodology employed to address these holistically, and further optimized from well to well to extend the operating envelope. The operator drilled a one-run curve and three-mile lateral with a motor-driven RSS BHA. This is a record single BHA run by the operator in the Delaware Basin that drilled the curve section planned at 11 deg /100′ and continued to drill three miles in the lateral to true well depth. The steering capability of the RSS BHA was optimized through multiple well iterations of BHA stiffness models. This optimum configuration reduced the main risk of connection fatigue due to high bending moments across the BHA components accumulated while rotating the BHA through the 11 DLS planned curve section. The changes in drilling practices, BHA, and Bit design, enhancing monitoring practices were effective to mitigate the severity of shocks from multiple vibration mechanisms encountered in the application. Identifying the tool damaging vibration mechanisms like High Frequency Torsional Oscillation (HFTO) in real time proved beneficial to optimize drilling parameters and prevent tool failure. Incorporating key features within the mud motor bearing section enabled configuring the mud pulse telemetry, RSS tool and Bit total flow area nozzle configuration for performance that closely matched the predicted steering performance of the RSS assembly. The inbuilt RSS high data processing capabilities enabled continuous trajectory control to deliver a low tortuosity wellbore. The paper describes the methodology and learning curve to reduce well time and improve drilling efficiency through BHA and bit design optimization, drilling parameters optimization and key technologies enabling for 8.5″ hole curve and three-mile lateral application. The reduction in drilling time realized will enable operators to deliver sustainable solutions to reduce carbon footprint to drill few wells while drilling them faster along with maximizing reservoir exposure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".