Overview articles

Reservoir Optimisation Software: the Next Generation  by David Millar, Drilling and Exploration World, February 2012

Overview of how optimisation can be used to improve the reliability of decision-making using reservoir simulation models, examples of the bottom-line benefits from case examples, and more details on the workflows that MEPO supports

Optimising Optimisation  by Gordon Cope, New Technology Magazine, November 2011

This article is a great description of how optimisation technology used in MEPO is changing the way people manage reservoirs, and includes interviews with a number of figures in the industry, as well as a summary of the technology and case examples

Optimisation Tool Reduces Risk for Well Positioning  by Karl Ludvig Heskestad, PESA News, June 2009

North Sea example of how MEPO can be used linked to PETREL static models to optimise well location for maximum cumulative production

TyumenNIIgiprogas Obtains Positive Results from Computer-Assisted History Matching of Dynamic Gas Reservoir,   SIS Case Study, 2008

Schlumberger case study combining MEPO with ECLIPSE, demonstrating a higher-quality match than trial-and-error history matching. The project generated several alternative history-matched models in just 4 weeks, compared to the 7-9 months using a traditional approach, as well as reducing uncertainty in predicted field performance and estimation of remaining reserves.

Workflows and case studies : MEPO linked to reservoir simulation models

Assessment of the Impact of Reservoir Uncertainty in History-Matching and Forecast Optimisations, Ahmed Sharif and Ralf Schulze-Riegert (SPT), SPE-SAS-318, April 2012

Describes a workflow for history matching and uncertainty quantification using MEPO, illustrated with a mature carbonate oilfield example.  Demonstrates the way multiple history-matches can be used to generate probabilistic production forecasts.

Well Path Optimization under Geological Uncertainty, R. Schulze-Riegert, M. Bagheri, M. Krosche, N. Kuck, Ma Dong and R. Saraf (SPT), Oil Gas European Magazine, February 2011

This paper describes a workflow using MEPO for determining optimal well trajectories by maximising cumulative production, based on a set of alternative geological model realisations representing the geological uncertainties.  Illustrated by a case example from the southern North Sea.

An Integrated Workflow for Gas Injection EOR and a Successful Application to a Heterogeneous Sandstone Reservoir in the Southern North Sea, N. Nishikiori and K. Sugai (AEDC), C. Norman and A. Onstein (Talisman Energy Norge), O. Melberg and T. Eilertsen (DONG E&P Norge).  IPTC 12025, presented at IPTC, Kuala Lumpur, December 2008

This study describes a workflow to screen gas injection EOR scenarios, and uses MEPO for the accelerated history matching part of the workflow, using Powell’s method.  The authors claim that the big advantage of the optimization process using MEPO was to reduce the simulation engineer’s subjectivity when exploring matching parameters.

Application of Global Optimization Methods for History Matching and Probabilistic Forecasting : Case Studies. M.K. Choudhary and S. Yoon (Chevron) and B.E. Ludvigsen (SPT).  SPE 105208, 2007

Two examples are presented using global optimisation methods in MEPO (evolutionary strategy and genetic algorithms) for history matching, uncertainty assessment and infill well optimisation.  The authors conclude that this approach resulted in a significant improvement in history match quality compared to earlier manual matches, and that the approach is well-suited for models with a large number of parameters, whilst also substantially reducing the time to match the models from about 8 weeks to 1.5 weeks.

Application of Global Optimisation Techniques for Model Validation and Prediction Scenarios for a North African Oil Field, B. Griess (OMV), A. Diab and R. Schulze-Riegert (SPT).  SPE 100193, 2006

In this example MEPO is used in a parallel computing environment to history-match a North African field, focusing on selected key wells which had been difficult to match manually. The study was used to evaluate the economic potential for new infill wells, potential side-track locations and ESP candidates, as well as enabling the authors to quantify uncertainty ranges for prediction cases and reserves calculations.

Enhancing Field Management in Siberia by Quantifying Production Uncertainties,  David E. Tipping and Maxim N. Deschenya (TNK-BP), Franz Deimbacher and Dmitry Kovyazin (Schlumberger). SPE 101808, 2006

This paper presents a history matching and uncertainty quantification study for a field in Western Siberia. Within a period of one week seven different history-matched models each yielding a unique production profile prediction were obtained with MEPO. It was shown that the traditional manual approach would take about 9 man-months to find a single matched model. From the results of the study it was shown that the assisted history matching approach can give major boosts to work productivity.

Integration of Geologic and Dynamic Models for History Matching, Medusa Field,  J. Lach, K. McMillen and R. Archer (Knowledge Reservoir), J. Holland and R. DePauw (Murphy E&P Co), and B. E. Ludvigsen (SPT). SPE 95930, 2005

MEPO was used to find multiple history matches in a quick, two week study of the T4B reservoir in the Medusa field, a stratigraphically confined syncline in the GOM.  Over 1000 simulations were run to evaluate a range of reservoir uncertainties. The history matching confirmed the reservoir connectivity across faulting and channel-levee-splay facies boundaries. The forecasts of future production showed a small spread in recovery, however the oil, gas and water rates were variable which can be significant for development planning in a deepwater setting.

Streamline-based History Matching with Application of Global Optimization Techniques, R.W. Schulze-Riegert, A. Diab, O. Haase (SPT), DGMK Spring conference, Celle, Germany 29-30 April 2004

Case study showing the application of global optimization techniques to full-field streamline simulation model of a heavy-oil field with 675,000 cells, 150 wells and 37 yrs of production.  Successfully demonstrates how MEPO and 3DSL can be combined to improve the understanding of large complex fields, and to quantify the uncertainties in production forecasts.

Workflows and case studies : MEPO optimising network and pipe flow models

Shtokman Flow Assurance Challenges : a systematic approach to uncertainties, H. Holm, P. Saha, V. Suleymanov, T. Vanvik and N. Hoyer, BHRG Conference, 2011

Case study using MEPO linked with OLGA flow assurance model to develop a systematic way of analyzing the risks and uncertainties associated with flow assurance on an ultralong (550 km) offshore pipeline for the Shtokman development in a harsh arctic environment in the Russian sector of the Barents Sea.

Gas Field Production System Optimization using Coupled Reservoir-Network Simulator and Optimization Framework, M.M. Nwakile, R. Schulze-Riegert and M.D. Trick, SPE 150770, 2011

Innovative study linking MEPO with the FORGAS network simulator to find optimal combinations of pipe sizes and compressor power, resulting in an improvement in NPV for the project.

Technology and methods used in MEPO

Hybrid Optimization Coupling EnKF and Evolutionary Algorithms for History Matching, by Ralf Schulze-Riegert, markus Krosch (SPT) and Oliver Pajonk (TU Braunschweig).  SPE 121965, 2009

This paper discusses an approach combining Ensemble Kalman Filter (EnKF) and Evolutionary Algorithm (EA) methods for history matching, showing how they complement one another and illustrating some of the leading-edge R&D that SPT invests in to ensure MEPO remains at the forefront of optimisation technology.

Modern Techniques for History Matching, by Ralf Schulze-Riegert Shawket Ghedan, 9th International Forum on Reservoir Simulation, Abu Dhabi, 2007

This paper presents an introduction to concepts and trends in modern History Matching. Special attention is paid to stochastic optimisation techniques. Uncertainties in reservoir data are summarized and provide the motivation for introducing new workflows in probabilistic forecasting. A distributed computing framework is described which facilitates deploying algorithms for an increasing number of complex simulation problems which require solutions in a brief time.

A Software Component Based Parallel Simulation and Optimisation Environment for Reservoir Simulation, M. Krosche, J.K. Axmann, O. Pajonk, R.W. Schulze-Riegert, O. Haase, DGMK-Tagungsbericht, 2005

This paper presents the technology behind MEPO and the development of next generation parallel optimization tool. Recent research on reservoir simulation has concentrated on alternative optimization methods in addition to gradient type techniques. Because of the long simulation times, parallel computing has become more important, in order to use the availability of cost efficient computing resources effectively. This technology favors optimization techniques that are scalable using parallel computing capabilities. Component based software engineering concepts are applied to develop an open, scalable and extensible system concept.

Combined Global and Local Optimization Techniques Applied to History Matching,  R.W. Schulze-Riegert, O. Haase, A. Nekrassov. SPE 79668, 2003

Demonstrates that a combination of global methods such as Evolution Strategy together with a Bayesian approach gives improved convergence when identifying history-matches. Illustrated with two case examples from Germany.