Streamline-based Production Data Integration Under Realistic Field Conditions: Experience in a Giant Middle-Eastern Reservoir

Qassab, Hisham M. Al; Pavlas, M.; Afaleg, Nabeel; Ali, Hamza; Kharghoria, Arun; He, Zhiguo; Lee, S.H.; Datta‐Gupta, Akhil

doi:10.2118/84079-ms

Cited by 20 publications

(24 citation statements)

References 28 publications

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“…No permeability enhancements were observed in the lower interval that represents low quality reservoir. These changes are consistent with those observed by Qassab et al 29 and were found to be geologically realistic. Fig.…”

Section: A Giant Middle-eastern Field Examplesupporting

confidence: 92%

“…The field has been under waterflood for the last 30 years. A detailed history matching of the water flood production response using streamline models was presented by Qassab et al 29 Here we repeat the exercise using a commercial finitedifference simulator and the generalized travel-time inversion. The initial geologic model was created based on well log derived porosity, facies information and 3-D seismic data.…”

Section: A Giant Middle-eastern Field Examplementioning

confidence: 99%

“…Cross-sections of the detailed geologic model and the corresponding upscaled model for both porosity and permeability distributions in the reservoir and the detailed upscaling methods can be found elsewhere. 29 The grid size for the upscaled model is 74×100×13. The initial water saturation in the simulation model was obtained using facies-based Jcurves and capillary-gravity equilibrium conditions.…”

Section: A Giant Middle-eastern Field Examplementioning

confidence: 99%

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Fast History Matching of Finite-Difference Models Using Streamline-Derived Sensitivities

Cheng

Kharghoria

et al. 2004

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractWe propose a novel approach to history matching finitedifference models that combines the advantage of the streamline models with the versatility of finite-difference simulation. Current streamline models are limited in their ability to incorporate complex physical processes and crossstreamline mechanisms in a computationally efficient manner. A unique feature of streamline models is their ability to efficiently compute the sensitivity of the production data with respect to reservoir parameters using a single flow simulation. These sensitivities define the relationship between changes in production response because of small changes in reservoir parameters and thus, form the basis for many history matching algorithms. In our approach, we utilize the streamline-derived sensitivities to facilitate history matching during finitedifference simulation. First, the velocity field from the finitedifference model is used to compute streamline trajectories, time of flight and parameter sensitivities. The sensitivities are then utilized in an inversion algorithm to update the reservoir model during finite-difference simulation.The use of finite-difference model allows us to account for detailed process physics and compressibility effects. Although the streamline-derived sensitivities are only approximate, they do not seem to noticeably impact the quality of the match or efficiency of the approach. For history matching, we use 'a generalized travel-time inversion' that is shown to be extremely robust because of its quasi-linear properties and converges in only a few iterations. The approach is very fast and avoids much of the subjective judgments and timeconsuming trial-and-errors associated with manual history matching. We demonstrate the power and utility of our approach using a synthetic example and two field examples. The first one is from a CO 2 pilot area in the Goldsmith San Andreas Unit, a dolomite formation in west Texas with over 20 years of waterflood production history. The second example is from a giant middle-eastern reservoir and involves history matching a multimillion cell geologic model with 16 injectors and 70 producers. The final model preserved all of the prior geologic constraints while matching 30 years of production history.

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Section: A Giant Middle-eastern Field Examplesupporting

confidence: 92%

Section: A Giant Middle-eastern Field Examplementioning

confidence: 99%

Section: A Giant Middle-eastern Field Examplementioning

confidence: 99%

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Fast History Matching of Finite-Difference Models Using Streamline-Derived Sensitivities

Cheng

Kharghoria

et al. 2004

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“…The streamline trajectories are based upon a single forward simulation, which can be from either a streamline or a finite-difference simulator. The GTTI history matching approach has been utilized in a large number of field applications (Cheng et al 2004;Hohl et al 2006;Qassab et al 2003;Rey et al 2009). …”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Streamline-Assisted History Matching Approach with Global and Local Parameter Updates

et al. 2010

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The effective strategies for traditional manual history matching commonly follow a structured approach with a sequence of adjustments from global to regional parameters followed by local changes in model properties, associated with matching for pressure (reservoir energy), flood front progression, and individual well performance. In contrast, many of the automatic history matching methods utilize parameter sensitivities or gradients to directly update the fine-scale reservoir properties, potentially combining elements at all of these scales. In this paper we present a hierarchical streamline-assisted history matching approach that emulates the traditional structured procedures. First, a probabilistic approach is used to understand the uncertainty in the large-scale static and dynamic parameters, and to calibrate these global parameters. In this global calibration, the intent is to develop multiple models that all sensibly match the field performance. This global calibration is followed by a streamline sensitivity-based deterministic model calibration for local permeability changes in which each of the distinct models created in the global match are history matched in additional detail.In the probabilistic global calibration, design of experiments and response surface methodologies with evolutionary algorithms are used to calibrate the global parameters. Typical parameters are regional pore volume multipliers, regional vertical and areal transmissibility multipliers, fault transmissibilities and aquifer strength. Key global parameters are first identified via a sensitivity analysis and an initial ensemble of models that span these parameters is created. The cases studied in this sensitivity analysis are used to construct a proxy model using experimental design and response surface analysis. An improved genetic algorithm with heat-bath sampling is used to generate an updated ensemble of models conditioned to static MDT pressures and total liquid rates at the wells, corresponding to a traditional pressure history match. Next, each ensemble member is updated using water-cut, GOR and flowing BHP via sensitivity-based local permeability calibration. We utilize streamline-derived analytic sensitivities to determine the spatial distribution and magnitude of these local changes.The proposed approach was tested by a 3D synthetic case and a field application. Our hierarchical approach appears to be stable in the sense that the local changes for permeability (inner loop) do not invalidate the pressure history match (outer loop). This is not entirely unexpected since the local permeability changes are regularized by a 'norm' constraint that minimizes deviations from the globally updated model. None the less, this is a very useful property in that it allows us to powerfully combine history matching procedures on global and local scales.

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“…The GTTI history matching technique has been applied in a large number of field cases with two-phase oil-water flow (Qassab et al, 2003;Cheng et al, 2004;Hohl et al, 2006) and more recently to three-phase and compressible conditions (Cheng et al, 2006). Oyerinde et al (2007) reported the difficulties in the calculation of the 'optimal' time shift in the presence of non-monotonic production profiles, particularly for the gas-oil ratio at the wells.…”

Section: Introductionmentioning

confidence: 99%

Assisted History Matching in an Offshore Turbidite Reservoir with Active Reservoir Management

Rey¹,

Ballin

Vitalis

et al. 2009

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Streamline-based assisted and automatic history matching techniques have shown great potential in reconciling high resolution geologic models to production data. Several field applications have demonstrated the efficiency of streamline-based sensitivity calculations together with the generalized travel time inversion (GTTI) for history matching. However, a limitation of the current GTTI based production data misfit calculations is that it is best suited for continuous and monotonic production histories. Field applications very often include active reservoir management decisions that involve well shut-in, recompletions and pattern conversions. These introduce significant discontinuities and non-monotonic effects in the production response.In this paper we propose an efficient and novel technique that handles production discontinuities through a transformation of the production data and by eliminating high frequency details in the transformed domain. The technique also reduces nonmonotonic behavior and results in a response more suitable for the GTTI based misfit calculations. Our proposed approach has been applied to an offshore turbidite reservoir with active reservoir management and highly detailed production information. The static model contains more than three-hundred-thousand cells, complex sand depositional distribution combined with fault structures, four pairs of injector, deviated producing wells and more than 8 years of production history. Previous history matching attempts using traditional approaches had difficulties matching production response at the individual well level. With our proposed modification to the GTTI approach, a significant improvement on the well match quality was obtained. Most importantly, by visualizing the streamlines and the dynamic adjustment of flow paths during history matching, we could easily identify the areas of inconsistency between the geologic model and the production data. The calibrated geologic model suggests communication within sand channels, differences in flow paths and barriers that have not been included in the previous geologic and seismic interpretation.

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Streamline-based Production Data Integration Under Realistic Field Conditions: Experience in a Giant Middle-Eastern Reservoir

Cited by 20 publications

References 28 publications

Fast History Matching of Finite-Difference Models Using Streamline-Derived Sensitivities

Fast History Matching of Finite-Difference Models Using Streamline-Derived Sensitivities

A Hierarchical Streamline-Assisted History Matching Approach with Global and Local Parameter Updates

Assisted History Matching in an Offshore Turbidite Reservoir with Active Reservoir Management

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