Case Study-A Real-Time Collaborative Workflow for Geosteering a Horizontal Well Offshore Eastern Canada
Bibliographic record
Abstract
Abstract Forward modeling geosteering software allows a horizontal well to be geologically steered by determining the stratigraphic position of the drillstring. Using forward modeling software one is able to make interpretations to correctly land the well in the reservoir, maximize the pay zone, optimize the well path, analyze reservoir properties and avoid crossing undesired bed boundaries or fluid contacts. An integrated suite of formation evaluation tools, software, and expertise is the best approach to maximizing the pay zone. A suite composed of MWD/LWD tools that capture real-time data and forward modeling while drilling software interpreted by a trained specialist interfaces with the operator's asset team to assist in the decision making process. A case study from offshore eastern Canada will be examined. Well X was drilled horizontally from a semisubmersible mobile drilling unit off of the east coast of Canada with the intention of reaching the target sands in the basin. As an aid in the interpretation of LWD logs while drilling the horizontal section, a software product called StrataSteer ™ developed by Halliburton was used. It operates with Sperry-Sun downhole tools and the INSITE ® information system. A data link in INSITE ® provides the real-time data for StrataSteer ™ to work with. Eastern Canadian operations provided the opportunity to show all involved partners in the drilling project the capabilities of forward modeling while drilling the above mentioned well from the drilling unit. In order to demonstrate the forward modeling capabilities built into the program Halliburton setup a computer in the customer's office as well as another one sitting at the offshore rig. Offset data was acquired from the customer and a geological model was constructed. From this model real-time collaborative decisions were made in the effort to reach the customers objectives. Introduction Sperry-Sun's StrataSteer ™ service is a fully integrated suite of tools, software, and expertise to land deviated and horizontal wells correctly in the reservoir and maximize the productive interval by maintaining an optimal well path. It is composed of MWD/LWD sensors that capture critical data, forward-modeling software that integrates these data with a geological model, a StrataSteer ™ service specialist trained to interpret the data to steer the well through geospace, and steerable drilling tools that control the wellbore trajectory. Sperry- Sun's StrataSteer ™ service specialists have training and expertise in horizontal well applications, directional drilling/surveying, structural geology, LWD resistivity modeling, log interpretation, and data communications. The StrataSteer ™ service specialists interpret all aspects of the drilling Pay zone steering, forward-modeling software enables a horizontal well to be steered geologically by determining the stratigraphic position of the drillstring. The geological earth model, directional well plan, and petrophysical data from real-time LWD logs are all integrated into a single modeling program. With pay zone steering software it is possible to make interpretations to:Correctly land the well in the reservoirMaximize the productive intervalOptimize the well pathAnalyze reservoir properties operation and interact collaboratively with the customer's team to assist in quality decision-makingAvoid crossing undesired bed boundaries or fluid contacts
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".