3D geological interpretation for geosteering of wells

Directional steering during drilling is essential for maximizing oil and gas recovery from petroleum reservoirs. Geosteering involves guiding the drill and placing wells in real-time based on geological data. By analyzing measurements taken during drilling—known as Logging While Drilling (LWD)—we can turn raw data into detailed descriptions of underground rock formations, giving us a clearer 3D picture of the area around the well. This deeper understanding helps us make more informed decisions about where to steer the drill. However, current 3D geosteering systems have limitations: a) There is no automated, real-time interpretation of 3D geological data or assessment of uncertainty while drilling. b) There is no standardized procedure for identifying all possible geological scenarios along the planned well path. c) The methods for processing electromagnetic (EM) measurements in 3D models are computationally demanding, especially when factoring in uncertainties. d) Machine Learning (ML) technology is underutilized in geosteering, and no existing methods can identify geological features around the well trajectory. Our primary goal is to develop and test a new workflow for automatic, real-time, 3D geological interpretation of LWD data to enhance well placement decisions. To achieve this, we will focus on several key objectives: • Around-Wellbore 3D Geomodeling: Create site-specific 3D models that capture all possible geological features expected along the well path. • Probabilistic 3D Inversion: Develop robust, efficient methods to process LWD data and quantify uncertainties in the geological interpretation. • Machine Learning Integration: Incorporate ML methods to enhance real-time geological interpretation, supplementing traditional approaches. • Verification: Validate the quality of the around-wellbore interpretation under various geological conditions through case studies.

Geosteering decisions are based on geological models. To make optimal decisions while drilling, it is necessary that geological models for the near-wellbore region are well-calibrated against measurements received while drilling, and that uncertainties are quantified. The current work processes for geosteering suffer from shortcomings. (i) It is highly challenging to geologically interpret LWD measurements in real-time for complex formations in an objective manner. (ii) It is highly challenging to update geomodels from available interpretations. (iii) There is a lack of a transparent, systematic, consistent and effective workflow for quantifying complex geological uncertainties. The primary objective of this project is to establish and verify a workflow for automatic, real-time, objective around-bit 3D geological interpretation of LWD logs for optimal well placement decision support while drilling in complex formations. The format of the interpretation supports future automatic updates of standard probabilistic geomodels. To achieve this, we will address the following secondary objectives: - Around-bit geomodelling. In the pre-drill phase, generate a set of site-specific geomodels that capture all possible geological configurations that can be expected along the well. This ensures that the basis for inversion is firmly grounded in the geological understanding of the drilling target, and constrains the spread of possible interpretation outcomes. - Probabilistic 3D inversion. Robust, accurate and computationally effective, ensemble-based inversion of LWD logs (including deep sensing EM logs) to quantify uncertainties. - ML methods. Machine learning methods for real-time geological interpretation to complement and expand on the analytical methods. - Case studies. Assess the quality of the around-bit interpretation under various geological conditions, applied to both synthetic and real data.