Reservoir management and production optimization - Bru21
Reservoir management and production optimization
Reservoir management and production optimization
Development and operation of oil and gas fields require decision-making under uncertainty on several horizons, from field development and optimal reservoir drainage strategies to the day-to-day decisions for operation of the current production infrastructure. BRU21 aims to develop computational tools and strategies to optimize production and manage uncertainty on both longer and shorter horizons. To achieve this, we use learning-based approaches to exploit the abundance of data, and combine with mechanistic models when data lacks information...read more
Program area team
- Lars Imsland, Prof. Optimization and Control
- Carl Fredrik Berg, Assoc. Prof. Reservoir Engineering
- Ashkan Jahanbani Ghahfarokhi, Assoc. Prof. Reservoir Engineering
- Alexey Pavlov, Prof. Petroleum Cybernetics
- Morten Hovd, Prof. Optimizationbased Control
- Damiano Varagnolo, Prof. Statistical Learning and Control
- Jon Kleppe, Prof. Reservoir Engineering
- Mathias Bellout, Researcher
- Mathilde Hotvedt, PhD candidate
- Joakim Rostrup Andersen, PhD candidate
- Tarek Diaa-Eldeen, PhD candidate
- Cuthbert Shang Wui Ng, PhD candidate
- Brage Strand Kristoffersen, PhD candidate
- Otávio Fonseca, PhD candidate
- Mammad Mirzayev, PostDoc
- Thiago Lima Silva, PostDoc
A hybrid data-driven and mechanistic model for production optimization in the oil and gas industry
Sponsor: Lundin Energy Norway AS
Hybrid modelling, also called grey-box modelling, combines the principles of two modelling approaches; physics-based, first-principle, mechanistic modelling and data-driven modelling. The idea is for the hybrid model to preserve the favorable characteristics of mechanistic models, such as physical interpretability and good extrapolation abilities, while exploiting the ability of data-driven models to capture unknown/unmodelled phenomena. In that manner, the hybrid model should have the potential to achieve high levels of accuracy and still be computationally feasible for utilization in real-time optimization.
Project result: Predicting choke performance of wells in the Edvard Grieg Field
A study comparing the accuracy of mechanistic, data-driven and hybrid choke models. There is value in data!
Assisted history matching, reservoir model update and optimization
Optimization of wells in a reservoir is a time consuming effort. Reservoir uncertainties and structural challenges can result in the procedure providing suboptimal locations. This project
investigates new types of parametrizations that, by replicating the decision-making process of geosteering, might enable a more efficient mapping of the search space. Features such as channels and faults can be dealt with and taken into account without increasing the complexity of the problem, yielding more realistic and productive well configurations.
Project result: Numerical geo-steering using neural networks on a reservoir model
An automatic well planning procedure that is fast and finds trajectories with high economic value
Data-driven control and optimization of oil and gas production systems
Post Doctoral fellow Thiago Lima
Main Supervisor Alexey Pavlov
This project deals with data-driven control and optimization methodologies for oil- and gas production systems in different time scales. On a long-term scale, the proposed approach combines simulation models with data-driven optimization to deal with unavailable gradient information and parametric uncertainties. On a shorter time scale, the adopted approach is a data-driven automatic optimization method referred to as Extremum-Seeking Control (ESC),
which allows one to achieve automatic optimization of steady-state behavior of an unknown plant. The methods developed in this project support a number of other BRU21 projects.
Project result: An improved method for optimal gas-lift allocation using automatic well testing
Ensuring a smooth transition, maximizing total oil rate and guaranteeing constraints are met at all times
Production optimization strategies for offshore production systems with water processing constraints
Mature fields are responsible for producing 70-80% of oil and gas worldwide. Due to natural water encroachment or employment of recovery techniques, mature fields have an increasing water-cut that can reach values above 97%. For each barrel of produced water there are costs related to pumping, storage, treatment and management. Therefore, produced water is a critical issue in the oil and gas industry as it economically affects the field asset and is environmentally challenging to surroundings. To add economic value to produced water, reinjection of produced water for enhanced oil recovery (EOR) is generally employed. This project seeks to investigate energy saving control and optimization
Project result: Improving the management of produced water in the Draugen field
Deploying optimization and control to reduce energy consumption and meet requirements in a complex system
Assisted history matching for petroleum reservoirs
This research project aims at introducing and implementing novel methods to address the computational challenges in the ensemble-based History Matching (HM), with the purpose of
reducing uncertainty in the model and, therefore, increasing the forecasting accuracy and production control efficiency. HM is the inverse modelling problem where an initial reservoir mathematical model is iteratively, manually or automatically, updated to match the production data. Ensemble methods in general, and particularly the Ensemble Kalman Filter (EnKF), have been widely used in HM problems. This research project is designed to introduce new and improve existing methods, such as the localization and covariance inflation, to enhance the EnKF’s performance in HM problems.
Data-driven reservoir modeling
PhD Candidate Cuthbert Shang Wui NG
Main Supervisor Ashkan Jahanbani Ghahfarokhi
Numerical reservoir simulations are widely applied to assist in decision-making related to reservoir management. However, more accurate models will need higher computation time. To mitigate this, a smart proxy model (SPM) has been developed. SPM applies a combination of advanced methods, such as optimization, statistics and data-driven techniques, which aim at significantly decreasing the run-time in any reservoir simulation task. The objective of this project is to improve the understanding of data-driven modelling and applying smart proxy modelling in reservoir simulations. The approach combines numerical simulations and data-driven techniques. Models are updated in realtime, creating realistic opportunities for real-time reservoir management in smart fields with uncertainty analysis.
Improved technology for production optimization, with focus on gas lift allocation
Post Doctoral fellow Mammad Mirzayev
Main Supervisor Lars Struen Imsland
Sponsor: Wintershall DEA
Maximizing oil production from any reservoir depends on decisions made on several horizons. The scope of this project is to optimize production across these horizons, with particular emphasis on gas lift allocation. Making the right decision is a key to safe and efficient operation. The project will develop solutions that optimize the allocation of gas lift using a combination of simple models and datadriven methods.
Optimization across time-scales in oil- and gas production
PhD Candidate Joakim Andersen
Main Supervisor Lars Struen Imsland
Some control and optimization problems face different objectives on different timescales, where the objectives may be in conflict. In this project we study efficient formulations and computational frameworks within the model predictive control (MPC) paradigm that handles both timescale issues and the multi-objective nature of these problems. An example of such a problem is production optimization in resource-constrained production. On a short term, one usually wants to optimize current production to maximize economic revenues. On a longer term, one may want to maximize the overall recovery of resources. Typically, some of the decision variables are common for the two objectives, but the objectives are not necessarily aligned: strategies that maximize current production may lead to non-optimal recovery. How to develop efficient computational tools for such multi-objective and
Integrated Reservoir Tool: FieldOpt
Post Doctoral fellow to be hired
Main Supervisor Morten Hovd
This project focuses on maintaining and further developing FieldOpt – NTNU’s open-source software platform for development of simulation-based optimization methods. FieldOpt’s main task is to codify, integrate and advance engineering expertise and optimization methodology to enhance educational, research and industry applications for efficient development and management of petroleum and other energy sources. FieldOpt provides extensive capabilities for prototyping, customization and real-case application of novel optimization routines coupled with state-of-the-art numerical simulators, e.g., models of subsurface fluid flow and upcoming renewable energy systems.