Tehran Institute for Advanced Studies (TeIAS)

/ Closed-Loop Field Development Optimization with Multipoint Geostatistics and Statistical Performance Assessment __ Mehrdad Gharib Shirangi


Closed-Loop Field Development Optimization with Multipoint Geostatistics and Statistical Performance Assessment

January 01, 2019


Khatam University, Building No2.
Address: Mollasadra Blvd., North Shirazi St., East Daneshvar St., No.17. See location on Google map


Dr. Mehrdad Gharib Shirangi

Senior Staff Data Scientist at Baker Hughes


The success of oil & gas operations strongly depends on making proper decisions on development plan (number, location, type, drilling sequence, and controls of new wells). Closed-loop field development (CLFD) optimization is a comprehensive framework for optimal development of oil & gas resources, presented first in 2015. CLFD involves three major steps: 1) optimization of full development plan based on current set of models, 2) drilling new wells and collecting new spatial and temporal (production) data, 3) model calibration based on all data. This process is repeated until the optimal number of wells is drilled. In this talk, we first present an efficient CLFD implementation for complex systems described by multipoint geostatistics (MPS). Model calibration is accomplished in two steps: conditioning to spatial data by a geostatistical simulation method, and conditioning to production data by optimization-based PCA. A statistical procedure (TruMAP) is presented to assess the performance of CLFD. In a massive computational experiment, involving about 10 million reservoir simulation runs that took about 320,000 CPU hours, the methodology is applied to an oil reservoir example for 25 different true-model cases. Application of a single-step of CLFD, improved the true NPV in 64%-80% of cases. The full CLFD procedure (with three steps) improved the true NPV in 96% of cases, with an average improvement of 37%. These results indicate the effectiveness of performing multiple steps of closed-loop optimization.


Mehrdad Gharib Shirangi is a Senior Staff Data Scientist at Baker Hughes, a GE Company. Before joining GE, he was a PhD researcher at Stanford University. Dr Shirangi’s current interests include prescriptive data analytics, machine learning for optimal decision making, and optimization of drilling & well completion in oil & gas operations. Shirangi holds BS degrees in mechanical engineering and petroleum engineering from Sharif University, an MS degree in petroleum engineering from University of Tulsa, and a PhD degree from Stanford University.