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.
Traditional geosteering workflows rely heavily on the expert oversight monitoring of incoming logging-while-drilling (LWD) data and continuous manual updating of geologic interpretations to identify key points in a well where directional decisions need be made. The labor-intensive nature of this work means that a remote supervisor is often limited to only watching 2 or 3 operations simultaneously. Previous studies (Maus, et. al. 2020) have described an algorithm capable of automatically producing multiple geosteering interpretations to reduce the human workload needed to follow along with the drilling operation. This study compares the results produced by this automated geosteering algorithm with interpretations generated through traditional means. The amount of effort required to produce an equivalent outcome is quantified in order to estimate the number of rigs an expert could potentially monitor. An automated geosteering algorithm was run in parallel with conventional geosteering for wells drilled in the Haynesville shale. Interpretations were checked for estimated landing point, modelled geologic structure throughout the lateral, and estimated footage of the wellbore in the target zone. In places where the automated interpretations provided improbable structures, control points were used to produce higher likelihood interpretations. The number of control points required was considered a proxy for the residual monitoring effort required of an operational geosteerer following the well using an automated system. The automated interpretations showed nearly 100% agreement with human geosteerers in terms of estimated formation strata drilled through. There were a small number of segments where manual intervention was required to fix unrealistic structures and improve agreement between the geosteering algorithm and the supervising experts. The capability of the automated system to produce and maintain reasonable interpretations for large stretches of the lateral proves it could be an asset for expanding the number of rigs being supervised by an off-site expert in a remote operations center. Time that would normally be spent updating and maintaining simple geologic interpretations can instead be devoted to analyzing the most challenging intervals in each well, resulting in a more efficient allocation of personnel. The ability to leverage automation and efficiently apply remote expertise is of increasing importance as operators aim to provide profitable returns at scale. This study quantifies the value potential for automation assisted geosteering workflows.
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