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Task 7: Field scale evaluation and history matching

These are the projects in Task 7.

Project title: Production optimization
Project manager: Geir Nævdal (IRIS)
Objective: The main objective of this project is to further develop robust optimization algorithms for efficient use in the petroleum production optimization problem. The secondary objectives are: Extension of ensemble-based production optimization to include IOR strategies (not only optimizing waterflooding). Investigation of how reduced order methods can reduce the computational effort needed (current workflows are computationally demanding due to the need for a large number of reservoir simulations)

Project title: Robust production optimization (PhD project)
Project manager(s): Reidar B. Bratvold UiS), Geir Nævdal (IRIS) and Aojie Hong UiS)
Objective: The main objective of this project is to develop robust optimization methods. The secondary objectives include: An optimal production strategy including geological uncertainties. How the decisions (optimal production strategies) may be different for robust optimization based on the geological uncertainties before and after history matching. (Geological uncertainties can be reduced by history matching). The impact of history matching on the results of robust optimization methods will be investigated through a Value-of-Information analysis.

Project title: Assemblage of different step size selection algorithms in reservoir production optimization(PhD project)
Project manager(s): Andreas S. Stordal (IRIS), Svein M. Skjæveland (UiS) and Yiteng Zhang (UiS)
Objective: The main objective of this project is to give a precise mathematical formulation of ensemble based optimization under geological uncertainty. The secondary objectives include: Improving the existing methodology using more sound mathematical insight. Understanding and improving the formulation of the objective function under uncertainty. Investigating the effect uncertainty has on several different parametrisations of the problem formulation.

Project title: Data assimilation using 4D seismic data
Project manager: Geir Nævdal (IRIS)
Objective: The primary objective of this project is to include 4D seismic data in ensemble based history matching for full fields. The secondary objectives include: Establishing real field(s) and gathering data required. Investigating in which form of 4D seismic data is most suitable for inclusion. Developing suitable rock physic model(s). Uncertainty quantification of the seismic data. Handle the big data amount of seismic data.

Project title: Interpretation of 4D seismic for compacting reservoirs
Project manager: Geir Nævdal (IRIS)
Objective: The main objective of this project is to address the extra complexity of compacting reservoirs when including 4D seismic data in history matching. The secondary objectives are: Working towards solving this problem with a data set from ConocoPhillips (Ekofisk). Initially we will focus on interpreting 4D AVO seismic data for updating saturations, pressures and porosities. (In this case the porosity is changing due to the effect of compaction.) In the second step we will use the interpreted data for ensemble-based history matching.

Project title: Data assimilation using 4-D seismic data (PostDoc TNO)
Project manager: Philippe Steeghs (TNO)
Objective: The main objective of this project is to improve and evaluate TNO’s ensemble-based history matching workflow in an extensive field case study. Moreover, the project will demonstrate the potential of the proposed method for 4D seismic monitoring and history matching.

Project title: 4D seismic and tracer data for coupled geomechanical/reservoir flow models
Project manager: Jarle Haukås (Schlumberger)
Objective: The main objective of the project is to investigate rational methods for building and updating coupled fluid flow/geomechanical models. The secondary objectives include: Linking 4D seismic observations to stress exchange in the reservoir and surrounding rock. Including the impact of faulted and fractured rock in history matching

Project title: Elastic full-waveform inversion (PhD project)
Project manager(s): Wiktor Weibull (UiS) and PhD student
Objective: Accurate and well-resolved estimates of the subsurface parameters from seismic data are essential for both exploration, as well as increased recovery of oil and gas reserves. This is particularly true as exploration moves towards subtler traps in complicated geological environments. At the same time, the ability to detect small changes in elastic parameters due to fluid substitution can greatly aid the development of increased oil recovery strategies. Full waveform inversion (FWI) is a well-known method for estimating subsurface parameters from seismic data. FWI can be used with single vintage seismic data to improve knowledge of the subsurface, or it can be used to estimate changes in subsurface parameters in a time-lapse fashion from 4D seismic data. This makes this technology well adapted for both the exploration and production stages of the petroleum value chain. Elastic FWI can be used to estimate both P-wave and S-wave impedances or their changes over time from multicomponent seismic data. There are still major challenges in applying FWI to field scale datasets. One problem is related to the high cost of the method. Another well known problem is the non-uniqueness of the problem. In terms of 4D seismic data, the major challenges are to reduce the artefacts introduced by repeatability errors and to include high enough frequencies in the inversion. Any attempt to use FWI must therefore tackle these challenges. In addition to developing strategies to tackle the above mentioned problems, we have also set the following key objectives: (1) To develop and test statistical methods of inference and to compare these with deterministic methods; (2) To use elastic FWI to estimate changes in elastic properties due to production from multicomponent seismic data acquired in permanent reservoir monitoring installations (PRMs). These estimated time-lapse changes will be compared with conventional approaches based on time-shift and time-strain measurements.

Project title: IOR Pilot Projects – Learning by Doing
Project manager: To be decided
Objective: The objective of the project is to resolve one of the key challenges that complicate the IOR project decision: Uncertainty. In this project we will use learning-by-doing (sequential learning) approaches leveraging Bayes’ theorem and dynamic programming to identify and value optimal information gathering sequences, given realistic constraints, for IOR pilot projects for the purpose of understanding and, possibly, reducing uncertainty.

Project title: The Value of Data and Data Analytics for IOR Operations
Project manager: To be decided
Objective: The objective of the project is to develop decision and data analytical methods specifically tailored to IOR decisions. This requires the identification of data and information relevant to IOR decisions. In most cases, the number of meaningful relationships in the data –those that speak to causality rather than correlation and testify to what really drives IOR relevant decision parameters – is orders of magnitude smaller than the data itself.

Project title: Data assimilation using 4D seismic and tracer data
Project manager: To be decided
Objective: This is a PhD project is linked to project 2.7.4 and the tracer projects, 2.5. The project will integrate previous research from these two together, and demonstrate the added value on field cases. The main objective of this project is to develop an ensemble-based workflow that can be used to assimilate multiple types of field data (4D seismic, tracer and production) into reservoir models in a most coherent way. For this purpose, the following research tasks will be carried out:

  • More efficient ways to handle big seismic data;
  • Optimal ways to assimilate different types of field data;
  • Proper uncertainty quantification for estimated reservoir models.

Project title: Reservoir Optimization and Model Evaluation
Project manager: To be decided
Objective: This is a PhD project is linked to project 2.6.1 and 2.7.1 and Theme 1. The project will integrate previous research performed in the Centre, and demonstrate the added value on field cases. The PhD candidate will focus on field scale modeling and simulation of EOR methods. Special focus will be on developing optimal strategies for e.g. polymer, smart water, or CO2 injection using synthetic models and the Open Porous Media (OPM) framework. In addition, a detailed study on impact of discretization methods for evaluation of EOR methods for oil fields will be
carried out. More specifically, the following objectives are identified:

  • Quantifying the value of e.g. polymer and smart water techniques on relevant North-Sea cases.
  • Evaluating the impact on decision making when using higher order numerical methods for simulating the subsurface flow.
  • Further development of ensemble-based optimization methods.

Project title: 4D seismic frequency-dependent AVO inversion to predict saturation-pressure changes
Project manager: To be decided
Objective: First, the methodology will be developed and run through feasibility tests and validation using synthetic seismic data, before moving to real data.Develop the theoretical foundation for frequency-dependent 4D seismic AVO inversion.

  • Performing feasibility analysis with synthetic data to predict elastic moduli changes, in addition to saturation and pressure changes, using 4D seismic AVO inversion including frequency dependence. These results will be compared with results obtained without including frequency dependence.
  • Apply the method to real 4D seismic data and compare the results to those obtained using conventional frequency independent AVO analysis and inversion.
  • Testing the workflow using frequency-dependent time-lapse in AVO attributes and spectral decomposition analysis, in order to predict fluid movements, and comparing the AVO attribute results without including frequency dependence.

The main problems that will be addressed in this project are:

  • Development of equations associated to time-lapse frequency-dependent AVO inversion.
  • Investigation of the effect of including frequency dependence on the ability to solve the inverse problem and look for optimal ways to optimize this solution.
  • Define an appropriate rock physics model linking saturation-pressure and elastic properties changes with frequency dependence.
  • Evaluate the ability of the method to distinguish between the effect of attenuation and associated dispersion, from other sources of frequency dependence on the elastic parameters.

   

  • Arne Stavland

    Task 1: Core scale

    Task leader: Arne Stavland, NORCE

    The aim of this task is to design novel experiments on core scale and develop models that capture the transport mechanisms observed. The deliverables of this task will be chemical systems that can improve the microscopic and microscopic sweep on clastic and chalk fields.

    Read more about the projects in Task 1 here

  • Udo Zimmermann

    Task 2: Mineral fluid reactions at nano/submicron scale

    Task leader: Udo Zimmermann, UiS

    The research focuses on rock-fluid interactions when injecting fluids into rock formations, clastic or chemical sedimentary rocks. We use electron microscopy, Raman spectroscopy, specific surface area measurements and X-Ray Diffraction for further investigations of EOR related experiments. The geology of the hydrocarbon bearing formations plays a significant role.

    Read more about the projects in Task 2 here

  • Espen Jettestuen

    Task 3: Pore scale

    Task leader: Espen Jettestuen, NORCE

    The focus in this task is to study the interplay between fluid transport, mineral reactions and oil recovery in reservoir rocks at pore scale. The main aspects are to identify the mechanisms that influence transport and reactions on the pore scale using experiments and numerical modeling, and then to evaluate if these mechanisms are important on the core scale.

    Read more about the projects in Task 3 here

  • Aksel Hiorth

    Task 4: Upscaling and environmental impact

    Task leader: Aksel Hiorth, UiS/NORCE

    The main objective is to translate the knowledge we have about EOR processes on core scale to field scale. The deliverables from this task will be simulation models and work flows that can be used to design IOR operations and interpret IOR implementations.

    Read more about the projects in Task 4 here

  • Tor Bjørnstad

    Task 5: Tracer technology

    Task leader: Tor Bjørnstad, IFE

    The objective is the development of tracer technology to measure reservoir properties and (changing) conditions during production. The most important condition is the (remaining) oil saturation, either in the flooded volume between wells (interwell examinations) or in the near-well region out to some 10 m from the well (single-well huff-and-puff examinations).

    Read more about the projects in Task 5 here

  • Robert Klöfkorn

    Task 6: Reservoir simulation tools

    Task leader: Robert Klöfkorn, NORCE

    The primary objective of this task is to advance the state-of-the-art of modeling and simulation in context of reservoirs. Such advance is needed to cope with the challenges arising from scientific questions and targets of The National IOR Centre of Norway.

    Read more about the projects in Task 6 here

  • Geir Nævdal

    Task 7: Field scale evaluation and history matching

    Task leader: Geir Nævdal, NORCE

    We do history matching using 4D seismic data, tuning reservoir parameters to obtain reservoir models that are matching actual observations. We are using ensemble based methods, running with a set of different realizations of the parameter set and use statistical methods to tune the parameters. The outcome is models that are aligned with actual observations.

    Read more about the projects in Task 7 here