Maximizing Value from Petrophysical Data

Practical Insights To Guide Development Decisions

Friday, November 20th, 10am-12pm CDT

Over the past decade much of the oil and gas industry has transitioned from conventional to unconventional developments, which has necessitated a shift from working with small to moderate size data sets towards working projects with thousands of wells. This transition occurred alongside the digital transformation, unlocking workflows that were previously inaccessible. This has necessitated petrophysics be performed at two different scales. Petrophysicists have continued to create value through detailed single-well interpretations that often incorporate specialist workflows and data sets. The second, newer source of petrophysical value creation has come through generating interpretations across thousands of wells. This has introduced new challenges and has required interpreters to adapt their workflows and techniques while developing specialized tools capable of accommodating them. Some of these challenges include: • Data conditioning and management • Incorporating spatially varied data • Utilizing custom parameters across large numbers of wells • Structural limitations in legacy software solutions Despite the challenges that come with this nascent branch of petrophysics the potential value add and its impact on other disciplines could eclipse that from more traditional workflows as the sophistication in these analyses continues to improve. For example, it is not unreasonable to believe that semi-regional to regional models of a wide swath of reservoir properties could serve to inform operational decisions and supplement the data acquired in horizontal drilling operations.

Program Outline

Moderated by Robert Barba, Integrated Energy Services Featured Presentations: Multi-Well Petrophysics: Requirements, Techniques, and Cross-Discipline Integration (Cameron Snow, Danomics) Production Calibrated Petrophysical Modeling (Jorge Viamontes, NuTech) Calibration and Uncertainty: Case Studies on Data, Uncertainty, and Reconciliation (Adam Staruiala, Cordax)