Data Analytics for Exploration and Production Workflow

The main skill is a working understanding and appreciation of analytical methodologies and statistical, data-driven techniques and soft-computing approaches to turn raw data into actionable knowledge.

The main problem is to gather hidden knowledge from the disparate and siloed Big Data sets traditionally interpreted. To identify patterns and marry interpretation to data-driven models to solve critical business problems, predict failures in facilities, optimize processes and maximize maintenance schedules.

The course starts with a review of historical digital oilfields and the analytical methodologies implemented to solve upstream business issues for surface facilities. We cover equipment and process management workflows identifying different platforms and datasets to address business issues. We detail building models from exploratory data analysis through operationalizing predictive models. We cover flow assurance issues and well/pipeline integrity case studies and then consider soft-computing techniques in drilling optimization and completions strategies in conventional and unconventional reservoirs. We close with several case studies for asset performance analysis and Integrated Planning as well as IIOT opportunities for structured/unstructured data.

Top Learning Objectives

  • Fully appreciate the Digital Oilfield workflows and architecture.
  • Identify through exploration of data, key performance indicators and statistically influential variables for building soft computing models.
  • Cover multiple case studies in equipment and process management for facility efficiency and maintenance scheduling.
  • How to build models to optimize drilling and completions strategies.
  • What makes up an enterprise Integrated Planning solution as illustrated by the EkoFisk case study.
  • Review Deep Learning and Machine Learning techniques and their applicability across the R&P value chain with best practices on Data Management and data-driven soft-computing workflows to attain business value propositions.
  • Learn the key oilfield analytic methodologies and application to the various subsurface data that are required during exploration and production
  • Apply the best practices in data management including subsurface data profiling, quality control, integration, enrichment and monitoring
  • Understand the key techniques in how to apply data analytics to various subsurface data during seismic analysis, reservoir characterization, simulation, drilling, completion and modelling

Who Should Attend?

This course is designed for but not limited to those who are directly involved with managing subsurface data information from exploration to production stages, and those who need a deeper understanding of the complexity and problems associated with Oil and Gas Data Analytics

  • Subsurface Data Analysts
  • Data Management Specialists
  • Upstream Geoscientists
  • Technical Data Information Executives
  • Data Management
  • Department Heads and Managers

Event Details

2nd – 4th March 2020
Abidjan, Ivory Coast