Development of autonomous drilling technologies requires the automated analysis and interpretation of Logging While Drilling (LWD) data to optimally land the well in the target formation and keep it in the pay zone. This paper presents a fully automated geosteering algorithm, which includes advanced LWD filtering, fault detection, correlation, tracking of multiple interpretations with associated probabilities and visualization using novel stratigraphic misfit heatmaps. Traditional geosteering uses manual stretch, compress and match techniques to correlate measurements along the subject wellbore against corresponding reference type logs. This results in a crude representation of strata by linear sections with offsets at fault locations. Instead of automating this manual process, we instead determine the possible interpretations as solutions of a geophysical inverse problem in which the total misfit between the subject and reference data is minimized. Interpretations are parameterized as discontinuous splines to accurately follow curved strata interjected by fault offsets. To account for ambiguities, multiple possible interpretations are continuously tracked in real time and assigned probabilities based on the misfit between the latest measurements and the reference data. Unrealistic solutions are suppressed by penalizing strong curvature and large fault offsets. Viable interpretations are simultaneously visualized in real time as paths on a novel stratigraphic misfit heat map, where they may be corroborated against valleys of minimal misfit between the subject and reference data. The user can guide the interpretation by setting control points on the heat map which the automated solutions must respect. The algorithm has been validated using wells from different regions across North America for which previous manual geosteering interpretations are available. The automated spline interpretations represent the actual curved strata more accurately than manual interpretations. Operationally, the automated interpretations can be provided within minutes compared to typical manual turn-around times of hours. Automation leads to more consistent and repeatable results, removing the subjectivity of manual interpretations.

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As development of the Barents Sea continues with new plays such as the Castberg, accurate specification of the local magnetic field is important to reliably infer the orientation of the bottomhole assembly (BHA) in horizontal drilling. Since magnetic fields at high latitudes vary spatially and temporally, one requires both spatial models and a way to capture temporal changes. Large temporal changes in the magnetic field can severly distort measured azimuths and therefore must be corrected for. This study, based on a report written for Petroleumstilsynet (Maus et al., 2017), shows that in regions of the Barents Sea within 50 km of a magnetic observatory, either the nearest observatory, interpolated infield referencing (IIFR), or the disturbance function (DF) method may be used for corrections in wellbore surveying to meet accuracy requirements. IIFR and DF will give better error reduction but are slightly more complicated to implement. At distances between 50 km and 250 km, the disturbance field (DF) method best meets accuracy requirements. In remote regions beyond 250 km, a local observatory must be deployed to meet the highest accuracy specifications, but the DF will still far outperform the other interpolated methods at such large distances from an existing observatory. Despite having focused on the Barents Sea region, this comparison of the accuracy of different spatial and temporal magnetic field mitigation methods for wellbore surveying is applicable to high latitude northern and southern regions across the globe.

Geosteering solves for a drill bit's stratigraphic location to optimally guide a wellbore through a target formation. Geosteering solutions focus on correlating a subject well's real-time measurements to a type log representative of the stratigraphic column. Traditionally, this is done by matching localized sections of each log with a combination of shifts and stretches applied. This is a single-solution-at-a-time approach where only the best correlation is represented, as determined subjectively by a human or via algorithmic minimization of differences between measurements (Maus et al 2020). A method that considers the full space of possible stratigraphic interpretations and assigns a complexity-related correctness likelihood to each would give geosteerers greater confidence in selecting the correct interpretation. This would be a prohibitively large space to explore via traditional optimization and inversion methods, but it is possible through application of a Viterbi algorithm to a Bayesian state space matrix. A set of 1440 synthetic geosteering trials was generated by producing a geologically realistic layer cake, passing a well trajectory through it, and simulating realistically corrupted gamma measurements (reflecting sampling rate, calibration error, and measurement noise). This gives a true solution for accuracy comparison, and a realistic log that can be interpreted as follows: a Bayesian state space matrix is constructed which captures the likelihood of correlation between subject well and type log measurements. Prior knowledge is used to inform a state-transition probability matrix. The Viterbi algorithm is then applied to the state space matrix and state-transition probability matrix to determine the highest likelihood interpretation. The trial data was split 80/20 into training and test sets. For the training data, three metrics were used to tune the algorithm: mean distance from true solution; misfit ratio against true solution, and algorithm runtime. On the remaining test data, highest-likelihood paths were compared to the interpretations generated by an existing residual-minimizing automated geosteering algorithm (Gee et al 2019). Performance was analyzed separately in the vertical, curve, and lateral, and solutions were spot-checked for reasonable behavior. Compared to the existing automated method, the Bayesian method produced interpretations with comparable performance in 59% of laterals, and significantly improved performance in 34% of laterals. It also returned results 30 times faster. These results held over several sets of tuning parameters suggesting robustness. A well-tuned Bayesian algorithm has been shown to outperform existing automated methods on performance and accuracy, signifying a potential step change in the space of automated geosteering. Viterbi is an established algorithm with many applications, but the splitting of stratigraphic mappings into a Bayesian state space and application of Viterbi is novel and allows for efficient, probabilistic solution-finding. The whole space of possible solutions can be considered, and implicitly gives solution likelihoods. The technique also accounts for the complexity of produced solutions.

Wellbore trajectories are a fundamental piece of data used for decisions throughout the oilfield. Trajectories are typically mapped through measurement-while-drilling (MWD) survey stations collected at 95ft intervals. Previous work suggests that this sparse sampling interval masks short segments of high curvature, negatively impacting workflows that consume this data (Stockhausen & Lesso, 2003; Baumgartner, et. al., 2019). This can come in the form of poorly estimating the true vertical depth of a well, poorly mapping geologic structure, and poorly quantifying the tortuosity of the wellpath. Several methods have previously been proposed to improve trajectory mapping by incorporating additional data collected between stationary surveys (Stockhausen & Lesso, 2003; Gutiérrez Carrilero, et al., 2018). Two sources of such data are continuous survey measurements and slide/rotate behaviors captured in slide sheets. Two methods of improving the wellbore trajectory mapping were compared in several extended reach lateral wellbores. The impact of the new trajectories on landing point selection, dip estimation, and wellbore tortuosity analysis was determined. One method took continuous inclination data and mapped directional changes between stationary surveys. The second used bit projections generated through automated-slide-sheet-analysis from real-time tool face data, estimating the location and direction of curvature produced by slide/rotate operations. These curvature estimations were used to predict wellbore shape between surveys. As a final check, in the curve sections of the wellbores, stationary surveys were collected at more frequent intervals (e.g., 31ft) to provide validation on the high-resolution trajectories and to understand the cost-benefit of simply surveying more frequently. Both methods of high-resolution trajectories imply that errors present in a 95ft course length survey are enough to impact decisions made when drilling an extended reach lateral. Landing point estimations were shifted in several cases by over 10ft, the approximate thickness of the target formation. Similar discrepancies in true vertical depth were observed along the length of the laterals. Both methods showed strong agreement through the curve sections of the wellbore, however this agreement weakened during the lateral where short slides and geological effects on rotary tendency reduced the accuracy of the automated-slide-sheet method. A discussion of the discrepancies between the two methods in laterals is included. Dogleg severity analysis confirmed that short sections of high curvature exist that are masked by traditional 95ft survey course lengths. Surveying at 31ft intervals improves the mapping of this tortuosity but still does not capture the full effects seen on continuous survey data. Previous work has suggested that typical wellbore trajectory mapping may be unsuitable for accurate analysis of things like geological structure and wellbore tortuosity analysis. Two methods are evaluated here that support those claims, suggesting that in the future high-resolution trajectories may be a necessity for accurate decision-making.