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Abstract The generation of reservoir simulation models that match field production data has been and is still a long-time industry challenge, not only for the time spent on history matching studies but also because of the non-uniqueness of the solution. This paper presents a new approach called "Hybrid Models" to accelerate this process and get more realistic history match models. Hundreds of stochastic possible geological models are produced and tested in regard to the dynamic data. The Hybrid model is a composite geological model, not only constrained by the initial well data but also with selected parts of the first realizations matching around some wells. This technique allows a relatively quick history matching process and results in a series of matched geological models. This process was applied in part of a heavy oil field (14 horizontal wells in fluvial reservoirs were considered), after 3 years of production. The objective was to explain and reproduce the high water-cut, oil rates, GOR and bottom-hole pressures in this part of the field. A complete uncertainty workflow was applied with sedimentological and petrophysical uncertainties as well as fluids and dynamic uncertainties. Results showed that static uncertainties were essential to get a coherent match and "Hybrid Model" technology was applied with success. The Hybrid model technique gives several matched geological models. All models have been carried out through forecasting keeping the present development plan, evaluating the potential impact of remaining static uncertainties. Dynamic uncertainties were also considered on one geological matched model. Several combinations of dynamic parameters have been computed to keep a match. Corresponding models have been transferred through forecasting. Final conclusions were that at fixed development plan, dynamic uncertainties are more to be considered and combined for the forecast than static ones. The use of the "Hybrid models" technique and the integration of static and dynamic properties as matching parameters have been shown to be efficient to produce accurate multiple production history matched models. From those models, it has been possible to quantify the remaining uncertainties in terms of future production and to propose new developments. Introduction The field considered in this study produces 8.5°API gravity of Extra Heavy Oil (EHO) with a viscosity at reservoir conditions between 1800- 3500cP. The EHO is upgraded to market of high quality 32 °API synthetic crude oil. The first phase of development is completed and includes more than 300 horizontal wells. The reservoir section, Middle Miocene in age, is subdivided in two main intervals. The lower part is mainly stacked unconsolidated sands deposited in a braided - meandering fluvial system. The upper part corresponds to sands encased into a shaly sequence associated to a fluvio-deltaic system with tidal influence. Approximately, 80% of producing wells are drilled in fluvial sands characterized by water production risks. In contrast, deltaic sands represent 20% of total oil production with little or no water risk. Due to extreme viscosity contrasts, after breakthrough the water cut in many wells increases rapidly. In order to get a good understanding of the production mechanism and then propose further development, it was clear that the history match phase was essential. This history match was not easy to reach, as the main parameter driving the reservoir dynamic behavior was the location of shale barriers within the model. Instead of modifying the geological model on a cell by cell basis (without keeping the geological coherency) a new approach called "Hybrid model" has been developed in order to get a relatively quick history match preserving the entire geological coherency. Static and Dynamic Uncertainties Before describing this "hybrid model" technique, it is essential to come back to the geological and dynamical models and their associated uncertainties. Those uncertainties will be assessed and ranked with respect to their relative impact on the history matching process, before planning the way forward.
Abstract History matching is an integral part of reservoir production forecasting, risk analysis and uncertainties quantification workflows. One has to cope with the non-uniqueness issue as history matching is an ill-posed inverse problem, due to a lack in constraints and data. Dealing with several history matched models is therefore critical and assisted history matching tools are of great interest to speed up the process. In practise, structural as well as petrophysical, PVT, SCAL, etc. data may be highly uncertain and the history matching process rarely tackles all these parameters in a single step. Classically, some of these parameters are considered as known while others are updated. This constitutes the 'by default' approach as all these parameters are interdependent and it may lead to sub-optimal history matched models. This manuscript presents an original history matching workflow that picks uncertain structural and petrophysical parameters anywhere in the "geomodeling to simulation" workflow, using a popular geomodeling software. Efficient parameterization technique of the geological model allows both geological and simulation models to be updated at the same time, preserving the consistency between each other. Using a versatile assisted history matching software, any external software such as a geomodeling software, may be automatically launched in batch mode from the constructed workflow. Background scripts then control each building step of the geological and simulation models, possibly capitalizing on an existing geomodel. This joint structural and petrophysical history matching leads to a more robust integrated geological stochastic reservoir model, as all uncertainties are simultaneously tackled and reduced. The results obtained on a 3D faulted synthetic waterflooding scenario demonstrate that this history matching approach is efficient since horizon depths, throw and transmissivity of faults as well as facies distribution, petrophysical and SCAL properties are simultaneously updated to explain the production history. Introduction One of the main outputs of reservoir engineering technical studies is to get reliable production forecasts. Within that framework, history matching of reservoir model(s) is pivotal but eventually not sufficient (Carter et al. 2006): key reservoir model inputs are updated until a satisfactory match is obtained between simulated and observed data. But the history matching process is an under-determined inverse problem. One will never gather enough data to constrain a unique reservoir model and potentially many models explain the data equally well. All of them should be considered for the production forecasts process. Moreover, the history matching criteria investigated has a non-smooth shape with many minima. This is a consequence of geological modeling and multiphase fluid flow simulations, based on non-linear coupled equations. It makes more complex the optimization process associated to the history matching loop. This problem is exacerbated when dealing with facies modeling as well as structural inputs, which is the case in the proposed application. History matching of structurally complex reservoirs may appear challenging because the uncertainty in reservoir geometry may impact production forecasts order(s) of magnitude bigger than the petrophysical related one. Structural uncertainty may reside in the poor quality of seismic data. Seismic data processing, migration, interpretation results as well as time depth conversion are themselves not unique, relying on subjective choices. In such case, traditional history matching approach considers reservoir geometry as fixed during the optimization process, updating the sole petrophysical and fluids related ones. Considering some parameters as constant (and thus artificially no more uncertain) while updating remaining ones may lead to sub-optimal history matched models.
In the past decade, there has been a great deal of research and progress in the development of computational methods to assist reservoir engineers in the arduous task of history matching their models. Developments in computer hardware and software and the use of geostatistics, optimization, and Monte Carlo methods are among the reasons for such progress. However, the requirements for a good history match also have been increasing. If, in the past, we were satisfied with a good data fit, this is definitely not the case anymore. Modern history matching is a much more comprehensive discipline. It entails geological modeling, geostatistics, reservoir simulation, scale issues, data analysis, deep understanding of reservoir mechanisms, interdisciplinary approaches, optimization methods, statistics, and inverse-problems theory.
Calibrating complex subsurface geological models against dynamic well observations yields to a challenging inverse problem which is known as history matching in oil and gas literature. The highly nonlinear nature of interactions and relationships between reservoir model parameters and well responses demand automated, robust and geologically consistent inversion techniques. In the recent years, there has been significant progress in automated history matching methods mostly categorized as gradient-based and ensemble-based techniques. Ensemble-based methods such as ensemble Kalman filter (EnKF) have proved to be successful in adjusting reservoir parameters to match observed dynamic data. To calibrate the reservoir model and update the distributed parameters in geologically robust way it is crucial to utilize a parameterization method. Parameterization techniques also aim to make the inversion process more efficient by reducing the dimension of the model parameter.
Recently in the fields of image processing and data mining, sparse parameterization and image reconstruction are gained considerable attention. We propose a novel automated history matching method by employing EnKF along with a sparsity-promoting parameterization technique. To improve the performance of EnKF in capturing complex geological features such as channelized reservoirs, we employ sparse constraints to construct the reservoir model from the observed dynamic well data. For the parameterization purpose we investigate utilizing various basis functions or models such as singular value decomposition (SVD) and discrete cosine transform (DCT). The combination of EnKF analysis equation with sparse reconstruction algorithm such as Matching Pursuit (MP) will enhance the inversion results. We also investigated effects of different geologically trained dictionaries or basis such as K-SVD and also the impact of combining various basis sets.
We applied the proposed sparsity-promoting EnKF to several numerical examples to estimate reservoir distributed properties from well production data. The enhanced EnKF method with sparse constrained parameterization showed promising improvement comparing to standard EnKF and also EnKF with standard parameterization. The proposed inversion technique propagates and updates an ensemble of geological models through integration steps and provides more consistent distributed parameter fields with the prior geology of the subsurface formation.
The sparsity-promoting EnKF is a derivative free history matching method which is also able to perform uncertainty assessment because of its ensemble based nature. Parameterizations along with sparse selection of basis functions make the calibrated solutions of this method more geologically consistent. This method is especially suitable for resolving more complex geological structures such as channelized formations which are generated with multi-point geostatistics techniques.