|Location||Abu Dhabi, United Arab Emirates|
|Date Posted||May 13, 2019|
Role and Responsibilities
Apply data analytics and artificial intelligence technologies to solve challenges in various subsurface petroleum engineering areas including reservoir dynamic characterization, reservoir management and field development to find opportunities for improving shareholder value (e.g. proactive identification of poor performance, optimize well locations, optimize reservoir management guidelines, optimize production and injection targets, maximize recovery factor and field development targets, etc.)
Identify, define and implement algorithmic solutions to create value from large spatial and temporal data sets, extract meaning from and interpret reservoir and well data, using industry and open-source using data analytics, machine learning and big data
Identify business cases and prototypes of cognitive tools for solving reservoir challenges.
Identify and define business cases, best practices, and corporate standard procedures for promoting the use of data-driven and artificial intelligence initiatives in reservoir management;
Document and share added-value and lessons learned from internal and outside use cases
Work with asset stakeholders to identify solution requirements, business cases and required interfaces, for implementing data-driven and artificial intelligence projects in reservoir management.
Qualifications and Education Requirements
Master or PhD degree in Petroleum Engineering, Math or Computer Science (or equivalent), with a professional career exposed to reservoir characterization, reservoir dynamics modeling, reservoir management, field development and related oilfield data-driven analytics, machine learning and/or artificial intelligence
Worked in five or more relevant projects; published five or more industry relevant technical papers.
Preferred Skills and Past Experience
Strong math skills in solving common linear algebra and challenging optimization problems in static and dynamic reservoir characterization, reservoir management and field development.
Working independently for coding workflows using open source libraries (Python, R, SQL or equivalent).
Knowledge and experience in statistical and data mining techniques: Regression, Simulation, Scenario Analysis, modeling, Clustering, Decision Trees, Neural Networks, Random Forest, Boosting, Classification Trees, text mining, recommender systems through social network analysis, automated reasoning, feature extraction.
Experience with commercial tools: Petrel, Roxar, Eclipse, Nexus, Tnav, CMG, Kappa, Petex, etc.
Experience analyzing data from 3rd party providers: POSC/PPDM, Petrel, PRODML, WITSML, OLE/ODBC, etc.
Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL, etc.
Presenting data for stakeholders using Spotfire, Qlik, PowerBI, Periscope, Business Objects, D3, ggplot, etc.
Understanding of the whole production system (Reservoir, wells, surface Network and Process)
Understanding of risk and uncertainty analysis, process improvement, systems engineering
Working individually and in a team, with a track record of project management, supervising others, and guiding stakeholders to most sustainable approaches