Adopting improved well data standardization methods and workflows to reduce cycle time and risk in the Deepwater Gulf of Mexico Basin

Abstract

Over 3000 wells have been drilled in the deepwater GOM. Although this activity has created an immense catalog of geologic information, geoscientists must deal with the diverse geology of the basin complicated by the lack of consistency and standardization within this database. Whether at the field level or performing a sub-regional or basin-wide study, confidence in the data and the ability to analyze it in a timely manner is critical. Standardization techniques and the adoption of improved workflows to high-grade data and visualize results will be discussed. This combination increases team efficiency which reduces project evaluation cycle-time and risk. All available log and core data from 1800 key wells across the basin were quality controlled, edited, and standardized to create a consistent well-to-well database. What became apparent during this process was that inconsistency of technologies across years, vendors and operators, a multitude of data sources, the lack of standards within subdisciplines, and differences in interpretation styles and terminology, have all contributed to significant variations within the database. For example, mudlogs from several wells across a subregional area would describe the same zone as gumbo, shale, silt, mudstone, or claystone; even wells within a field were subject to these discrepancies. With some data, subtle but critical inconsistencies were also discovered. Workflows were modified to account for these variations so that the final geological outcome was consistent across the basin. With smaller workforces and the requirement to be more productive, cut costs and reduce risk, it is imperative to have reliable, consistent data to be able to perform rapid assessments of opportunities in large basins such as the Gulf of Mexico. Workflows have been developed and critical tools identified for efficient creation of highly quality-controlled well log datasets. Adopting the methods discussed reduces time to acquire critical information from days or weeks to minutes.

AAPG Datapages/Search and Discovery Article #90291 ©2017 AAPG Annual Convention and Exhibition, Houston, Texas, April 2-5, 2017