In preparation for the SPE Applied Technology Workshop (ATW) held in Brugge in June 2008, a unique benchmark project was organized to test the combined use of waterflooding-optimization and history-matching methods in a closed-loop workflow. The benchmark was organized in the form of an interactive competition during the months preceding the ATW. The goal set for the exercise was to create a set of history-matched reservoir models and then to find an optimal waterflooding strategy for an oil field containing 20 producers and 10 injectors that can each be controlled by three inflow-control valves (ICVs). A synthetic data set was made available to the participants by TNO, consisting of well-log data, the structure of the reservoir, 10 years of production data, inverted time-lapse seismic data, and other information necessary for the exercise. The parameters to be estimated during the history match were permeability, porosity, and net-to gross-(NTG) thickness ratio. The optimized production strategy was tested on a synthetic truth model developed by TNO, which was also used to generate the production data and inverted time-lapse seismic. Because of time and practical constraints, a full closed-loop exercise was not possible; however, the participants could obtain the response to their production strategy after 10 years, update their models, and resubmit a revised production strategy for the final 10 years of production. In total, nine groups participated in the exercise. The spread of the net present value (NPV) obtained by the different participants is on the order of 10%. The highest result that was obtained is only 3% below the optimized case determined for the known truth field. Although not an objective of this exercise, it was shown that the increase in NPV as a result of having three control intervals per well instead of one was considerable (approximately 20%). The results also showed that the NPV achieved with the flooding strategy that was updated after additional production data became available was consistently higher than before the data became available.

Results of the Brugge Benchmark Study for Flooding Optimization and History Matching

Closed-loop reservoir management is a combination of model-based optimization and data assimilation (computer-assisted history matching), also referred to as ‘real-time reservoir management’, ‘smart reservoir management’ or ‘closed-loop optimization’. The aim is to maximize reservoir performance, in terms of recovery or financial measures, over the life of the reservoir by changing reservoir management from a periodic to a near-continuous process. The key sources of inspiration for our work are measurement and control theory as used in the process industry and data assimilation techniques as used in meteorology and oceanography. We present results of a numerical example to illustrate the scope for closed-loop water flooding using real-time production data under uncertain reservoir conditions. The example concerns a 12-well water flood in a channelized reservoir. Optimization was performed using a reservoir simulator with functionality for adjoint-based life cycle optimization under rate and pressure constraints. Data assimilation was performed using the ensemble Kalman filter. Applying an optimization frequency of respectively once per 4 years, once per 2 years, once per year and once per 30 days resulted in an increase of net present value (NPV) with 6.68, 8.29, 8.30 and 8.71% compared to a conventional reactive control strategy. Moreover, the results for the 30-day cycle were very close (0.15% lower NPV) to those obtained by open-loop optimization using the ‘true’ reservoir model. We illustrate that for closed-loop reservoir management with a fixed well configuration, the use of considerably different reservoir models may lead to near-identical results in terms of NPV. This implies that in such cases the essential information may be represented with a much less complex model than suggested by the large number of grid blocks in typical reservoir models. We also illustrate that the optimal rates and pressures as obtained by openor closedloop optimization are often too irregular to be practically applicable. Fortunately, just as is the case for the data assimilation problem, the flooding optimization problem usually contains many more control variables than necessary, allowing for optimization of long-term reservoir performance while maintaining freedom to perform short-term production optimization. Introduction Our work aims at increased reservoir performance, in terms of recovery or financial measures, using a measurement and control approach to reservoir management. This idea has been around for many years in different forms, often centered around attempts to improve reservoir characterization from a geosciences perspective; see e.g. Chierici (1992). Moreover, recently ‘closed-loop’ or ‘real-time’ approaches to hydrocarbon production have received growing attention as part of various industry initiatives with names as ‘smart fields’, ‘i-fields’, ‘e-fields’, ‘self-learning reservoir management’ or ‘integrated operations’; see Jansen et al. (2005) for some further references. However, whereas the focus of most of these initiatives is primarily on optimization of short-term production, in our work we concentrate on life-cycle optimization, i.e. on processes at a timescale from years to tens of years. We perform reservoir flooding optimization, based on numerical simulation models, in combination with frequent model updating through data assimilation (computer-assisted history matching). This approach has lately also been referred to as ‘closed-loop reservoir modeling’ or ‘closed-loop production optimization’ and some recent references will be discussed below. In contrast to the geosciences-focused approach, we emphasize the need to focus on those elements of the modeling process that can both be verified from measurements and that bear relevance to controllable parameters such as well locations or, in particular, production parameter settings. The underlying hypothesis is that “It will be possible to significantly increase life-cycle value by changing reservoir management from a batch-type to a near-continuous model-based controlled activity.”

Closed Loop Reservoir Management

The state of natural gas

https://research-api.cbs.dk/ws/files/62226758/vahid_sohrabpour_et_al_artificial_intelligence_in_supply_chain_management_publishersversion.pdf

Artificial intelligence in supply chain management: A systematic literature review

Machine Learning based Digital Twin Framework for Production Optimization in Petrochemical Industry

Self-Learning Reservoir Management

We propose a decision-making approach for optimizing the profitability of hydrocarbon reservoirs. The proposed approach addresses the overwhelming complexity of the overall optimization problem by suggesting an oilfield operations hierarchy that entails different time scales. We discuss system identification, optimization, and control that are appropriate at various levels of the hierarchy and capitalize on the abilities of permanently instrumented and remotely actuated fields. Optimization is performed in real-time and is based on feedback. We provide details on real-time identification of hybrid models and their use at the scheduling and supervisory control levels. Case studies using field-calibrated simulation data demonstrate the applicability and value of the proposed approach. Directions for future development are given.

Luigi Saputelli

Papers

Self-Learning Reservoir Management

Real-time reservoir management: A multiscale adaptive optimization and control approach

State of the Art of Artificial Intelligence and Predictive Analytics in the E&P Industry: A Technology Survey

A Critical Overview of Artificial Neural Network Applications in the Context of Continuous Oil Field Optimization

Real-Time Decision-making for Value Creation while Drilling