Bibliographic record
Abstract
Abstract The geosteering process should not be seen as a process solely designated for the most expensive or highest profile horizontal wells. It can be regarded as another tool for improving the odds of success by remaining in the productive zone for longer periods of drilling. Also, it can be used to optimize the positioning of a horizontal wellbore in the sweet spots within the reservoir. The current process has been successfully applied to large infill drilling programs at over 40 wells for heavy oil, tight gas, conventional oil and gas plays and for Mannville coalbed methane (CBM) in Alberta. The service has been provided irrespective of location, as long as the Wellsite Information Transfer Standard Markup Language (WITSML)/Pason Satellite service is available. Exploration and production (E&P) companies are continuously being driven to reduce the cost per barrel of oil equivalent (BOE). E&P needs and technologies related to advanced and accurate directional drilling, communication of vital data in real-time through the internet, as well as reduced cycle time associated with advanced forward-looking 3D geo-modelling and visualization technologies (Figure 1), are currently converging. The motivation to reduce costs has been responsible for advancing the horizontal well geosteering process by incorporating the Measurement While Drilling (MWD) tool into mainstream drilling practices. The universal economic benefits gained can be found in all resource play types (conventional oil and gas, heavy oil, tight gas and coalbed methane). It is important to note that the process described here is essentially collaborative. For best results, there must be cooperation between the E&P operational geologist, wellsite geologist, directional driller and geo-modelling staff, as well as the engineering consultants involved in the project (i.e. the team as a whole). Introduction Reducing Costs and Increasing Performance for Optimal Well Results Whether drilling a long reach horizontal well in heavy oil or a tight gas play, the basic requirements for a successful well are:Planning the optimal path based on the current knowledge of integrated geological/geophysical models.Monitoring the progress of the well through real-time updates by well profiles and 3D visualizations.Continuously re-mapping to identify the true stratigraphic position (TSP) of the bit relative to the reservoir. This information is used to provide advice to the drilling team for staying in the zone of interest while drilling.Timely reporting on the updated road map for the horizontal well to provide the information necessary for drilling ahead of the bit. Depending on the depth and/or rock type, the speed of drilling can range from very fast (200 m/hr in shallow heavy oil horizontals) to very slow (3 to 10 m/hr in tight formations). For fast or slower drilling, the geosteering process is used as a planning and monitoring tool. This reduces guesswork in the drilling process which translates into less drilling time for a given well, ultimately decreasing the total cost and increasing profits. The 3D geo-models can be updated every few minutes for structural changes and periodically for characterization of gamma ray (GR), resistivity and other reservoir attributes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".