Improving Well Placement and Reservoir Mapping Using Multi-Interval Inversion of Deep and Extra-Deep LWD Resistivity Measurements

Deep and extra-deep logging-while-drilling (LWD) resistivity measurements are commonly used in the well construction phase to land and navigate within complex geology. The measurements’ complexity often prohibits their visual interpretation for steering decisions. Such decisions are made after data inversion and based on the two-dimensional (2D) or three-dimensional (3D) visualizations of resulting inversion models (or pictures). While the main objective of inversion is achieving a good match between measured and simulated data, its result is a resistivity model that is used for geological interpretation. To deliver a high-confidence interpretation, many important considerations impacting the inversion result have to be addressed, such as accuracy, uncertainty, geological sense, etc. In this paper, we present a goal-oriented approach where one-dimensional (1D) inversion with lateral regularization is run on several data intervals simultaneously. The algorithm can balance both data match and model continuity, delivering geologically meaningful models. The inversion algorithm is generic enough to accommodate any set of measurements with arbitrary weight settings enabling goal-oriented (multiresolution) inversion. Having deep and extra-deep measurements available in the same well, it is reasonable to run multi-interval inversion on the former for near-wellbore analysis or the latter for large-scale reservoir mapping. The developed algorithm delivers more geologically robust resistivity models with improved lateral continuity of layers’ resistivity, thickness, and boundary positions. The level of additional lateral regularization between models can be controlled by a user based on available knowledge about geology or preconfigured for automated execution. Further QC of data match and tool sensitivity ranges helps to understand the validity of features mapped in the inversion results. In summary, the paper focuses on the analysis of the quality of the inversion result and the reliability of interpretation, covering aspects of • Lateral continuity vs. data misfit • Parametric models vs. picture • Confidence in geological interpretation vs. depth of detection • Real-time vs. pre- and post-well processing • Tuning for a particular application vs. general black-box approach We developed a new data inversion algorithm for the deep and extra-deep resistivity tools. The approach delivers laterally consistent resistivity models without compromising data match. In cases of sharp structural changes such as faults, it also may be used as an indicator for intervals better suitable for 2D/3D processing. At the same time, the resulting model preserves quantitative parameters (boundaries, resistivity/anisotropy values, dip) for interpretation and allows the estimation of confidence for those parameters. The robustness of the method is demonstrated on synthetic benchmarks and field data from the North Sea.