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.
Three-mile laterals have become more common over the last five years of onshore US drilling. They are especially commonplace in the Appalachian and Permian basins and are used to overcome limited surface access for drill pads and for economic reasons. These long laterals pose significant wellbore positioning and anti-collision challenges. Horizontal position error grows at 2% (or more) of the lateral length per degree of wellbore azimuth error. This work addresses these wellbore positioning challenges with a new and significant improvement in magnetic field determination. With this procedure, multi-well pads with tightly spaced three-mile laterals can be drilled without compromising anti-collision standards or horizontal placement goals. Most commonly in the US land market, tightly spaced laterals are 1-2 miles in length and make use of In-Field Referencing (IFR-1) magnetic models built from airborne geophysical surveys to ensure proper positioning and avoid well collisions. For more challenging pad designs, such as three-mile laterals, a new method has been developed to combine an IFR-1 magnetic model with a near-well magnetic theodolite measurement to build a more precise magnetic model and positioning tool code. Specifically, the declination error terms in the ISCWSA (Industry Steering Committee on Wellbore Survey Accuracy) Error Model can shrink beyond the IFR-1 tool code specifications. This reduces the horizontal uncertainty in the ellipse of uncertainty (EOU) by upwards of 40% when compared to the MWD tool code standard. A study was conducted on a typical well and pad design for three-mile laterals in the Marcellus Shale in Pennsylvania. We find that the horizontal uncertainty with the MWD tool code at two and three miles of reach to be 206 feet and 303 feet, respectively. With the new tool code enabled by this body of work, we calculate the horizontal uncertainty at two and three miles of reach to be 120 feet and 174 feet, respectively. These results clearly show that this technique enables three-mile laterals to be drilled more safely and more tightly together. It is preferable for well pad design and lateral spacing to be determined by drilling and reservoir economics rather than collision concerns. Well planners and reservoir engineers can now safely access more of the reservoir from a single pad with longer laterals. This work is novel because it combines a ground based, near-well, magnetic measurement with an airborne derived IFR-1 model. This allows for a greater reduction in positioning uncertainty than has been available in the past. The application of this method to three-mile laterals is also new and has a profound impact on being able to plan optimally spaced wells and avoiding collisions.
Developing resources in congested fields requires precise drilling to keep the wellbore in the correct stratigraphic unit and avoid collisions with existing wellbores. Automated geosteering is an advantageous method because it eliminates subjective and lengthy manual interpretation. We present an algorithm that incorporates drilling dynamics data to estimate rock strength, correlates to nearby offset wells, and locates the drill bit in stratigraphy. This new method is faster and more objective than the traditional manual interpretation. During well planning, gamma ray intensity, sonic, and geologic logs from nearby wells are recovered and used to calculate the Confined Compressive Strength (CCS) of the stratigraphic column using empirical and physical formulas. While drilling, the algorithm receives gamma ray intensity and drilling dynamics data (e.g. weight on bit, rate of penetration, revolutions per minute, fluid flow, and differential pressure) from the subject well and calculates the Mechanical Specific Energy (MSE) of the penetrated rock. Finally, the real time MSE and gamma from the subject well are correlated automatically with the previously generated CCS and gamma logs of the offset wells to locate the drill bit in stratigraphy. This approach to automated geosteering was tested on a large database of subject wells and offset wells from multiple basins in North America. We find that the nearby CCS logs generated from sonic data are significantly correlated with MSE values from drilling dynamics data from the subject wellbore. CCS and MSE values can therefore be used as complementary rock strength parameters for each stratigraphic unit. Furthermore, the automated geosteering algorithm has been successful in estimating the drill bit position in the stratigraphic column during real-time drilling simulations of incoming drilling dynamics data. Incorporating rock strength estimation and matching is a complementary and valuable technique to the standard gamma log interpretation for steering a wellbore to a geologic target. The automated algorithm facilitates the simultaneous correlation, in conjunction with gamma ray intensity, against multiple offset wells. Traditionally, geosteering is accomplished by manual comparison of LWD (Logging While Drilling) gamma ray intensity with nearby gamma logs from existing offset wells. Our approach is a significant advancement because it incorporates gamma ray data and previously unused drilling dynamics data, while automating the geosteering removes laborious and subjective processes. Mature oil fields contain considerable information on rock strength and our automated geosteering algorithm makes optimal use of this information to improve wellbore positioning.
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