Cockrell School of Engineering
The University of Texas at Austin


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Graduate Seminar Speaker, Jorge L. Landa

Date

Monday, November 24, 2014

Time

03:00pm - 04:00pm

Location

CPE 2.204

Description

Jorge L. Landa, Research Consultant in Reservoir Engineering with Chevron, will present a talk entitled "Probabilistic History Matching and Forecasting in Mature Reservoirs" as part of the Claude R. Hocott Graduate Seminar Series.

Abstract:

This technical talk deals with the problem of estimating forecast uncertainty in mature reservoirs and it is an upgraded version of the SPE Distinguished Lecturer presentation "Assessment of Forecast Uncertainty in Mature Reservoirs".

"Modern and efficient reservoir management is imperative, given the ever-increasing demand for oil. Making the right decision on reservoir development utilizing all available data in a timely manner is the key to a successful operation. For mature reservoirs, this requires high-quality uncertainty assessment of long-term performance forecast estimations. One critical and difficult component of the total uncertainty in forecasting is the one that stems from the implicit uncertainty in the geological and reservoir simulation models. In fact, regardless of the amount of reservoir data that we collect, there is no way to define the reservoir model uniquely. This reality suggests that we use an integrated probabilistic framework - Probabilistic History Matching - and incorporate production data into the reservoir model to reduce the associated uncertainty in reservoir characterization and performance forecasting.

The technical challenge is in obtaining a probabilistic description of the reservoir model. For mature reservoirs, this implies finding not one, but a large number of reservoir models that are consistent not only with the geological data but also with the production data. Using substitute low-computational cost mathematical models for the complex flow simulation combined with Monte Carlo simulation within a probabilistic framework, and utilizing available high-performance computing resources, it is feasible to find multiple solutions to the probabilistic history matching problem. These solutions, in turn, can be used to describe uncertainty with probabilistic forecasts. This presentation demonstrates the practicality of an approach to solve this critical problem using a real field example."

Bio:

Jorge Landa is a research consultant in reservoir engineering with Chevron U.S.A. in Houston TX. His technical areas of interest are in numerical reservoir flow simulation, history matching, uncertainty assessment and integration of 4D seismic into reservoir models. He holds MS and PhD degrees in Petroleum Engineering from Stanford University and a Mechanical Engineering degree from Universidad de Buenos Aires in Argentina.