Water, Water Everywhere, But Not a Drop to Drink: Navigating Basin Model Calibration in Unconventional Basins Containing Thousands of Wells With Poor Quality Data

Abstract

Unconventional basins have posed new challenges to basin modelers because of their data intensive nature. Unlike most offshore frontier basins, which may contain tens to hundreds of wells, onshore developed basins often contain tens of thousands of wells, making it a challenge to integrate and calibrate to each well from both a time and software capacity perspective. Additionally, many of these wells were drilled decades ago and the data is often of questionable quality and misleading. Here, methods for thermal data cleansing, integration, and calibration are highlighted with examples from the Powder River Basin. The Powder River Basin contains over 5,000 wells with temperature data from DSTs, with most containing only one to two temperature readings per well. This presents a challenge in (1) determining the quality of the readings, (2) incorporating all good quality data into the model, instead requiring calibration “spot checking” due to time constraints, and (3) working with software not designed for unconventional basins. In this project, DST data was aggregated into a single pseudo‐well location per section, reducing the well count to under 500 wells. Each pseudo‐well was then assessed for data quality and thermally calibrated in order to determine present day thermal trends for the basin. This technique helped quickly identify thermal anomalies in the basin. Thermal maturity data was also integrated into the project first through data collection from literature review and vendor databases, and then through additional sampling of cuttings and core from wells drilled decades ago. Preliminary basin models that utilized exclusively public and vendor data were later recognized to be of very poor quality and oftentimes misleading. Rock Eval data (VRETmax) typically under‐estimated thermal maturity, and analysis from cuttings were often of significantly poorer quality than from core. Additionally, vitrinite reflectance often over‐estimated thermal maturity due to oxidation. Ultimately, the team utilized a petrographic correction workflow to assess maturity which was corroborated with a fluid inclusion study. These data integration methods enabled straight‐forward calibration of basin models that were trusted by the team. Additionally, this work supports our need to use multiple types of maturity data to understand the quality and uncertainty associated with each data set.

AAPG Datapages/Search and Discovery Article #90349 © 2019 AAPG Hedberg Conference, The Evolution of Petroleum Systems Analysis: Changing of the Guard from Late Mature Experts to Peak Generating Staff, Houston, Texas, March 4-6, 2019