Eric Bhark

Results of the First Norne Field Case on History Matching and Recovery Optimization Using Production and 4D Seismic Data

In preparation for the SPE Applied Technology Workshop, ``Use of 4D seismic and production data for history matching and optimization – application to Norne (Norway)’’ held in Trondheim 14-16 June 2011, a unique test case (Norne E-segment) study based on real field data of a brown field offshore Norway was organized to evaluate and compare mathematical methods for history matching as well as methods on optimal production strategy and/or enhanced oil recovery. The integrated data set provided an opportunity to discuss emerging and classical history matching and optimization methods after being tested using real field data. The participants of this comparative case study were expected to come up with a history matched model preferably using an integration of production and time-lapse seismic data and with an optimal production strategy for the remaining recoverable resources for the future period. Participants were allowed to suggest techniques to enhance recovery. Taking into account that the Norne benchmark case is a case study based on real data and no one exactly knows the true answer, participants and delegates were encouraged to discuss the methods, results and challenges during the course of the workshop, and thus in this case there are no winners or losers. Everyone who participated gained experience during the course of the exercise. Participants were asked to history match the model until the end of 2004 and optimally predict the production (oil, water and gas rates) performance until the end of 2008. Participants were from different universities in collaboration with other research organizations namely Stanford University in collaboration with IBM and Chevron, TU Delft in collaboration with TNO, Texas A&M University, and NTNU in collaboration with Sintef. This paper summarizes the presented results from these groups and the outcome of the discussion of the workshop delegates. Introduction The Center of Integrated Operation in petroleum industry at NTNU (IO Center) in conjunction with the Society of Petroleum Engineers (SPE) organized an applied technology workshop about the use of real data from the Norne Field. The workshop attracted 80 delegates and international speakers from more than ten countries all over the world, namely the United States of America, Saudi Arabia, the Netherlands, Brazil, Denmark, Angola, Nigeria, the United Kingdom, Russia, India, and Norway. The uniqueness of this workshop was that it addressed for the first time a comparative case study that uses real field data that includes time lapse seismic data. The purpose of reservoir management is to control operations to maximize both shortand long-term production. This consists of life-cycle optimization based on reservoir model uncertainties together with model updating by production measurements, timelapse seismic data and other available data. Time-lapse seismic data helps to determine reservoir changes that occur with time and can be used as a new dimension in history matching since they contain information about fluid movement and pressure changes between and beyond the wells. The well production schedule and history are provided for the period from Dec. 1997 to Dec. 2004 and comprise the observation data for the history match. A previous full field calibration was performed by the operator to match the history up until 2003. The reservoir attributes previously calibrated include fault transmissibility multipliers, regional relative permeability parameters, and large-scale (absolute) permeability and porosity heterogeneity using regional and constant multipliers, in total defining a global history match for a single (structural) reservoir description. The exercise was to improve the match and then perform a recovery optimization. In total five groups participated in this exercise and four presented their results during the workshop (see Table 1). The limited number of participants was due to the inaccesability of the Norne database (license limitation) to commercial companies. Table 1: Participants to the case study. University/Company Main Contributors Stanford University, Chevron & IBM Amit Suman, Drosos Kourounis, Tapan Mukerji and Khalid Aziz Delft / TNO Slawomir Szklarz, Lies Peters and Remus Hanea Texas A&M Eric Bhark, Rey Alvaro, Mohan Sharma, Akhil DattaGupta NTNU Ola T. Miljeteig, Richard Rwechungura, Anass Ammar and Jon Kleppe University of Texas Austin Reza Tavakoli and Mary Wheeler** **Did not present the results in the workshop Description of the Norne Field The Norne Field is located in the blocks 6608/10 and 6508/10 on a horst block in the southern part of the Nordland II area in the Norwegian Sea. The rocks within the Norne reservoir are of Late Triassic to Middle Jurassic age. The present geological model consists of five reservoir zones. They are the Garn, Not, Ile, Tofte and Tilje. Oil is mainly found in the Ile and Tofte Formations, and gas in the Garn formation. The sandstones are buried at a depth of 2500-2700 m. The porosity is in the range of 25-30% while permeability varies from 20 to 2500 mD (Steffensen and Karstad, 1995; Osdal et al., 2006). The data consist of near, mid and far stack 3D seismic data acquired in 2001, 2003 and 2004. More information about the Norne field, provided data and first case release are given in Chapter 5 (Rwechungura et al., 2010). The first package includes the E-segment of the Norne field; other benchmarks will include larger parts of the field. Further, the seismic data were also separated to suit the requirement of coverage of the Esegment only. Accordingly, the E-segment was chosen because it has the highest quality seismic data of the entire field. The E-segment of the Norne Field consists of 8733 active grids and 5 wells as of the end of 2004, i.e., 2 injectors and 3 producers. Participants were given a password to access the Norne database through the website www.ipt.ntnu.no/~norne. Description of the Exercise The exercise was defined six months prior to the workshop on History Matching and EOR optimization using both production and time lapse seismic data. This benchmark case considers the time frame from 1997 to 2004 for history matching and 2005 to 2008 for recovery optimization. The actual 2004 simulation model containing all information and properties was given. In addition, production and injection data from 1997 to the end of 2004, and 4D-seismic data for the same period (2003-2001 and 2004-2001) were provided. These data are the basis for the history match performed by participants. The following was the defined workflow:  Download the Eclipse Norne model and import it into their reservoir simulator. The production history for 1997-2004, reports and all required data are given in the website http://www.ipt.ntnu.no/~norne.  Participants were required to history match the model until the end of 2004 and predict the production (oil, water and gas rates) performance until end of 2008.  Using the history matched results from above, create an optimal production strategy for the remaining recoverable resources for the future period. Participants might also suggest techniques to enhance recovery, since significant amount of the recoverable reserves were already produced by the end of October 2008.  The format for the production strategy should contain time, pressure (BHP) or flow rates for the wells.  The following constraints should apply to the strategy: 1. For each injector well the maximum FBHP = 450 bar 2. For each producer well the minimum FBHP = 150 bar 3. For each injector well the maximum water rate = 12000 Sm/day 4. For each producer well the maximum liquid rate = 6000 Sm/day 5. Maximum water-cut = 95% 6. A maximum of two wells can be sidetracked to increase recovery  The following economic parameters were given : Oil price 75 US

Subsurface Flow Model Calibration with a Spectral-Domain Parameterization Adaptive to Grid Connectivity and Prior Model Information

per bbl Discount rate 10% reference time is January 2005 Cost of water handling/injection 6 US

A generalized grid connectivity–based parameterization for subsurface flow model calibration

per bbl Cost of gas injection 1.2 US

An adaptively scaled frequency-domain parameterization for history matching

per Mscf (M = 1000) Cost of a new side-tracked well 65 million US

Streamline-based integration of time-lapse seismic and production data into petroleum reservoir models

Participants could also assume their own parameters related to other EOR methods, e.g., surfactants, polymers and low salinity water flooding.  Discuss and compare results of the achieved recovery factor. General Methods for all the Groups As stated before, four groups presented their results in the applied technology workshop in June 2011 in Trondheim. In this paper we present the details of the work from three groups, namely Texas A&M University, the Norwegian University of Science and Technology (NTNU) and Stanford University. The history matching results from TU Delft were previously published (Szklarz S et al. 2011). They used the Ensemble Kalman Filter (EnKF) method for history matching and did not perform an optimization or an enhanced oil recovery (EOR) strategy; therefore, this paper does report the results from TU Delft. A summary of the methods applied for history matching and recovery optimization by each group is in Table 2. To perform history matching, Stanford University started by dimensionality reduction of the reservoir parameters using their principal component analysis (PCA) and then application of particle swarm optimization (PSO) for history matching. For subsequent optimization they used a derivative free method, Hook Jeeves Direct Search (HJDS). The group from Texas A&M first engaged in multiscale reparameterization of the permeability field using the Grid Connectivity-based Transform (GCT) and calibrated the reduced permeability to production data using a Quasi-Newton method. Thereafter they applied a streamline-based method to integrate the 4D seismic data. Last, the Texas A&M group increased recovery and optimized the production forecast by first draining the oil pockets through side tracking, and by second applying a streamline-based method to equalize the arrival time of fluid phase fronts at all producers. The group from NTNU applied manual history matching techniques that included qualitative

A Multiscale Workflow for History Matching in Structured and Unstructured Grid Geometries

A heterogeneity parameterization is introduced to mitigate the challenges associated with field-scale subsurface flow model calibration. The estimated geologic parameter field is mapped to and updated in a low-dimensional transform domain using a linear transformation basis. The basis vectors are the eigenvectors of a Laplacian matrix that is constructed using grid connectivity information and the main features in the prior geologic model. Because the grid connectivity information is computed within a small multipoint stencil, the Laplacian is always sparse and amenable to efficient decomposition. The resulting basis vectors are ordered from large to small scale and include prior-specific spatial features. Therefore, the variability in reservoir property distribution can be effectively represented by projecting the property field onto subspaces spanned by an increasing number of leading basis vectors, each incorporating additional heterogeneity features into the model description. This property lends itself to a multiscale calibration algorithm where basis elements are sequentially included to refine the heterogeneity characterization to a level of complexity supported by the resolution of available data. While the method can benefit from prior information, when the prior is unavailable or unreliable the transformation can reduce to a discrete Fourier expansion with robust model-independent parameterization properties.We present the derivation and theoretical justification of the transformation basis and review its advantageous properties for heterogeneity parameterization including efficient one-time construction, applicability to any grid geometry, and strong prior model compression performance. The multiscale calibration workflow begins by updating the prior model using a parameterized multiplier field that is superimposed onto the grid and assigned an initial value of unity at each cell. The multiplier is sequentially refined from the coarse to finer scales during minimization of well production data misfit. This method permits selective updating of heterogeneity at locations and levels of detail sensitive to the data, otherwise leaving the prior unchanged as desired. The parameterization approach is applied to calibrate several petroleum reservoir models using an adaptive multiscale algorithm.