Integrating Drilling Dynamics into Automated Geosteering

Abstract

Abstract 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.