LWD Azimuthal Laterolog Resistivity Logging in Unconventional Reservoirs
Bibliographic record
Abstract
Abstract The development of unconventional oil and gas resources is critically dependent on accurate geosteering of horizontal wells. A new type of LWD Azimuthal Laterolog Resistivity logging tool has been introduced that is specifically designed for geosteering and formation evaluation in complex carbonates, shale-gas, shale-oil, and coal-bed methane formations. The new tool is able to detect approach to a distant parallel bed-boundary of contrasting resistivity that is either higher or lower than the borehole environment. This enables predictive geosteering if the tool is not installed too far from the drill-bit. Conventional wave-propagation tools respond mainly to nearby conductive anomalies, which can limit their application in geosteering shale-oil wells where the target formation is often lower resistivity than those on upper or lower boundaries, or when the borehole traverses a fault and the target formation is higher resistivity and located in an unknown direction. Laterolog types of tools are capable of accurate measurement over a wide range of resistivity from 0.2 – 20,000 ohm.meters, enabling hydrocarbon saturation calculation even in tight carbonate formations. Many carbonate formations, and most coal seams, produce from natural fractures or vugs. The new tool has thin-bed resolution of 0.14 meters, enabling it to image the more productive zones for real-time production analysis while drilling, with significant potential cost-savings. The GRT tool has been commercial for two years, accumulating several thousands of hours of drilling time in operations provided by several independent MWD service companies. Successful geosteering jobs have been completed in the Montney, Bakken, Viking (Canada), Niobrara, and Avalon (USA) unconventional formations. The first test in China was in Talimu Oilfield, where the new tool was able to reproduce logs from a wireline Dual Laterolog in resistivities ranging from 20,000 – 100,000 ohm.meters. Tests have also been run in Tuha and Changqing (Sulige) oil and gas fields. A description of the tool operating theory, design objectives, unique real-time geosteering display, and logging software is presented. Some results from three years of operations in North America and China will be discussed along with logs and brief case studies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".