An Innovative MEMS-Based MWD Method for Directional Drilling
Bibliographic record
Abstract
Abstract To overcome the challenges and disadvantages of conventional simple vertical drilling methods, directional drilling, which directs a wellbore along a predefined trajectory to the desired target, is widely used nowadays. However, directional drilling requires much better surveying accuracy of boreholes drilled for oil and gas exploration. Currently, this surveying accuracy is provided by the measurement while drilling (MWD) tools, which can be categorized into two types: magnetometer-based systems and gyroscope-based systems. Unfortunately, both systems have some practical problems, such as magnetic interference of environmental effects, inaccurate trajectories between survey stations and non-real-time measurements. The main objective of this paper is to propose a reliable, real-time, and low-cost MWD surveying system and overcome the limitations of traditional systems. In recent years, micro-electro-mechanical system (MEMS) sensors have been widely used in navigation fields due to their small size, rigidity, and low-cost consumption. Thus, MEMS-based MWD technology has gained much attention and can potentially be applied in very small diameter well drilling activities with satisfactory precision. In this paper, a new method to provide continuous measurement information about the wellbore trajectory during the drilling process was developed. A strapdown inertial navigation system (SINS) technology, consisting of three accelerometers and three gyroscopes, has been utilized to integrate a rotary steerable system (RSS) and MWD tool into one drilling probe. This proposed MWD surveying system eliminates the size constraint of conventional inertial sensors. It also overcomes the real-time problems aroused from distance between MWD system and drill bit. Due to the large noise in MEMS sensors and the strong nonlinearity in MEMS-based SINS especially under large shocks and high vibrations, unscented Kalman filtering (UKF) was used in this paper to enhance the performance of the drilling system. Stand-alone MEMS-based SINS only provides a short-term accurate navigation solution. Therefore, the following aiding information was also used as updates for the MEMS-based SINS in the drilling procedure: the length and velocity information of the total pipelines, the zero velocity information during periodic stop intervals and azimuth information from the magnetometers. The proposed method was validated in two typical scenarios. Results showed that the proposed method can offer more accurate position and attitude than the conventional extended Kalman filtering (EKF). In conclusion, the feasibility of the innovatively continuous borehole surveying method had been demonstrated in this paper. The presented MEMS-based MWD method for directional drilling can eliminate the costly nonmagnetic drill collars for the magnetometers, overcome the size limitation of gyroscope-based MWD systems for small wells drilling, survey the borehole continuously without interrupting the drilling process, and improve the overall accuracy by utilizing UKF technique.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".