Subsurface Analytics Case Study; Reservoir Simulation and Modeling of Highly Complex Offshore Field in Malaysia, Using Artificial Intelligent and Machine Learning

Data-Driven subsurface modeling technology has been proven, for the past few years, to yield technical and commercial success in several oil fields worldwide. A data-driven model is constructed for the first time for an oil field onshore Abu Dhabi, and used for evaluation of a reservoir with substantial reserves and comprehensive development plan; for the purpose of predicting production rates, dynamic reservoir pressure and water saturation, improving reservoir understanding, supporting field development optimization and identifying optimum infill well locations. The objective is to provide the asset with a decision-support tool to make better field development planning and management. The subject reservoir is a low permeability carbonate reservoir and characterized by lateral and vertical variations in its reservoir rocks and fluid properties. More than 8 years of Phase-I development and production/injection data and extensive amount of well tests and log data (SCAL, PVT, MDT) from more than 37 wells were used to construct the Data Driven Model for this asset. This new modeling technology, (TDM), integrates reservoir engineering analytical techniques with Artificial Intelligence, Machine Learning & Data Mining in order to formulate an empirical and spatiotemporally calibrated full field model. In this work, it is leveraged with other conventional reservoir modeling and management tools such as streamline modeling, isobaric maps and flooding conformance. Several analyses were performed using the full field data-driven model; complementing the existing conventional numerical model. The accomplishments of the data-driven reservoir model for this project included, but not limited to, comprehensive history matching (including blind validation) and then forecast of Oil rate, GOR, WC, reservoir pressure and water saturation, injection optimization, and choke size optimization. The results generated by the data-driven model proved to be quite eye-opening for the asset management; as the model was able to identify potential areas of improving field efficiency and cost reduction. When combined with numerical techniques, the calibrated data-driven model assist to obtain a reliable short term forecast in a shorter time and help make quick decisions on day-to-day operational optimization aspects. The use of facts (all field measurements) instead of human biases, pre-conceived notions, and gross approximations distinguishes data-driven modeling from other existing modeling technologies. Its innovative combination of Artificial Intelligence and Machine Learning (the technologies that are transforming all industries in the 21st century) with reservoir engineering, reservoir modeling and reservoir management clearly demonstrates the potentials that these pattern recognition technologies offer to the upstream oil and gas industry for its realistic digital transformation.

Time-lapse seismic (4D seismic) has emerged as a critical tool in understanding reservoir dynamics, particularly in complex deep-water turbidite sandstone systems. The application of 4D seismic in the Niger Delta's deep-water turbidite reservoirs has significantly enhanced reservoir management and further development, particularly in modeling waterflood fronts and identifying remaining and/or by-passed oil. This study explored the integration of 4D seismic data into reservoir performance history matching for modeling waterflood fronts and identifying bypassed or remaining oil. Deep-water turbidites are characterized by heterogeneous and high-permeability channel systems, leading to challenges in predicting waterflood efficiency and ultimate recovery. By combining 4D seismic with dynamic reservoir simulation, key insights into saturation changes (remaining oil), reservoir connectivity, and fluid flow pathways were obtained. This workflow involved processing an Ocean-Bottom-Node (OBN) 4D seismic data to extract changes in acoustic impedance, which were linked to saturation and pressure variations. These changes were then incorporated into the reservoir performance history matching to refine the reservoir model, ensuring better alignment with observed production and injection data. The seismic-derived waterflood front tracking enhanced the understanding of the reservoir sweep efficiency and flood front location, highlighting areas with bypassed oil due to intra-reservoir heterogeneities, stratigraphic barriers, sub-seismic faults, or suboptimal well placement. This integrated approach has been used in the Ufoma deepwater oilfield further development as a case study. It did not only improve the accuracy of the existing 3D Earth reservoir models but also supported the optimization of the field development strategy by successfully targeting the remaining oil pockets in the further development project for the reservoir. It also led to improved reservoir characterization and modeling in the Ufoma field reservoir management (i.e., WRFM - History Matching and Prediction) and has proven to be a valuable tool in maximizing hydrocarbon recovery in these complex geological channel sands bodies. This work further demonstrates the efficacy of this integrated approach, leading to improved well placement for maximum oil recovery and optimized field development strategies for the Ufoma oilfield in deepwater Niger-Delta.

Simulation studies and three-dimensional (3-D) reservoir modeling were conducted as part of an integrated geologie, petrophysical, and geophysical effort to better define the distribution of remaining oil and the opportunities for a more effective recovery of remaining hydrocarbons. Two 3-D reservoir models—a 2000-ac window model and a fieldwide model—were built using a cycle-based geologic framework and rock-fabric–dependent petrophysical properties. A comprehensive sensitivity study on volumetrics was conducted using the fieldwide model, and reservoir simulation was performed in a 1600-ac area in the window model. Original oil in place (OOIP) is a complex function of log-data quality, mapping parameters, vertical resolution of the 3-D grid, oil-water contact, and cutoff values in porosity, permeability, and water saturation. The high vertical-resolution 3-D model calculates higher OOIP than the 36-layer cycle-based model by 8 to 30%, depending on the cutoff criteria. Because permeability is a function of porosity and rock fabric, the permeability cutoff is equivalent to rock-fabric-dependent porosity or water saturation cutoffs and is less sensitive to grid vertical resolution than porosity and water saturation cutoffs. The simulation study was divided into two phases: sensitivity analysis and history matching. The sensitivity study was used to evaluate and rank the importance of reservoir parameters affecting production performance. During simulation, oil relative permeability for primary recovery has a strong effect on recovery from waterflooding. Because fractures and breccias are common in testing and core data, negative skin factors (or effective wellbore radii) were used to simulate near-wellbore fractures, and permeability values in the lower Wichita were modified to simulate karst-related breccias. Through history matching, optimal fluid and rock properties were determined.

High resolution sequence stratigraphical methods have been used in the detailed geological modeling of an element of the Ness formation of a North Sea field. This updated geological description has been used as input to the stochastic geological model MOHERES. 14 realizations of facies architecture and petrophysical properties have been generated. The stochastically generated realizations have been scaled up to a refined element of an existing deterministic, history matched reservoir simulation model for the full Upper Brent reservoir. The upscaled versions of the realizations have been connected to the full field model, and reservoir simulations have been performed to compare twelve years of production history with simulated results for well production, RFT- and PLT-data. Six of the realizations gave good to very good results when compared to measured production data. These realizations were used for simulation of the future production performance to the year 2010, making it possible to estimate its uncertainty.

Reservoir model history matching is a complex, time-consuming, and resource intensive process that needs to be carried out carefully for building reliable predictive tools to manage Oil & Gas assets. Reservoir models encompass detailed geological description representing subsurface heterogeneities that influence its dynamics. To intelligently manage and preserve the complexity of the reservoir models, an artificial intelligence, Progressive-Recursive Self-Organizing Maps (PR-SOM), algorithm was developed. PR-SOM is an unsupervised artificial intelligence neural network algorithm that classifies the reservoir grid cells into progressive reservoir parameters to identify similarly adjoining regions. The algorithm explores and identifies model geo-bodies with similarities and dissimilarities in a progressive and recursive manner. This allows history matching to be conducted on much smaller subsets of the reservoir model of similar geological features. In this work, an artificial intelligence (AI) algorithm was applied, first, to guide the reservoir-wide history match processes. Next, the algorithm was applied to fine-tune well performance using information form well testing and historical data. The algorithm uses both static properties (permeability, rock quality indices, porosity, flow zonation … etc.) and dynamic properties (pressure or saturation) to construct similarities matrix. The results show that the clusters’s growth is progressive, controlled and quality assured by accounting for the controlling reservoir parameters. The number of mapped regions (clusters) is determined by optimizing the similarity matrix recursively. The quality of the global reservoir history match shows the effectiveness of the algorithm, better quality matching for historical production data, and fewer iterations (i.e. less simulation runs). The process is repeated to calibrate the reservoir model near wellbores by limiting the AI algorithm to only the drainage regions seen from well tests and historical data. The results show that employed AI-guided history matching revealed similarities and dissimilarities in the reservoir model. That not only enhance field and well match, but also allowed us to maintain the heterogeneity contrasts inherited from the Earth model. The advanced algorithm was successfully used to assess the extent of geological heterogeneity and its impact on reservoir dynamics, to enhance history match quality, minimize human interaction, and to reduce computational requirements.

Recently, the Ensemble Kalman Filter (EnKF) has emerged as an effective tool for performing continuous updating of petroleum reservoir simulation models. The method is firmly grounded on the theory of Kalman filters and sequential Monte Carlo techniques. The ability of the method to sequentially update the spatial properties in petroleum reservoir models, such as permeability and porosity, by integrating the dynamic production data makes it a very attractive approach. Moreover, the method takes into account the production uncertainty in the reservoir models by using error covariance matrices for the measurement vector (Production and injection rates, Gas-Oil ratio, Steam-Oil ratio, etc.) and the state vector (pressure, saturation, permeability, porosity). Similar to the traditional Kalman filter, the covariance matrices have to be tuned to reflect the uncertainty in the model and the measurements. We consider two unconventional oil reservoir models: 1) highly heterogeneous black-oil reservoir model, and 2) heterogeneous SAGD reservoir model. The results will demonstrate the advantage of using the localized EnKF for effective history matching using ensemble sizes relatively lower than what otherwise would be required with the ordinary EnKF. The results will also show the advantages of using prior knowledge available from the wells (permeability and porosity measurements) to generate initial realizations. One of the main practical advantages of history matching using the EnKF over traditional optimization based approaches is its low computational effort. The computational cost is dominated by Monte Carlo simulation of the ensemble of models only. Thus, significant computational time saving is possible by running each of the ensemble simulations on independent processors in a parallel mode. Moreover, the method can be easily integrated with any commercial reservoir simulation software.

Reservoir simulation results currently provide the basis for important reservoir engineering decisions; grid complexity and non-linearity of these models demand high computational time and memory. The physics-based simulation process must be repeated to increase model prediction accuracy or to perform history matching; consequently, the simulation process is often time-consuming. This paper describes a new methodology based on a deep neural network (DNN) technique, the graph convolutional neural network (G-CNN). G-CNN increases the modeling prediction speed and efficiency by compressing the computational time and memory usage of the reservoir simulation. A G-CNN model was used to perform the reservoir simulations described. This new methodology combines physics-based and data-driven models in reservoir simulation. The workflow generates training datasets, enabling intelligent sampling of the reservoir production data in the G-CNN training process. Bottomhole pressure constraints were set for all simulations. The production data generated by the reservoir model, with the mesh connectivity information, is used to generate the G-CNN model. This approach can be viewed as hybrid data-driven, retaining the underlying physics of the reservoir simulator. The resulting G-CNN model can perform reservoir simulations for any computational grid and production time horizon. The method uses convolutional neural network and mesh connections in a fully differentiable scheme to compress the simulation state size and learns the reservoir dynamics on this compressed form. G-CNN analysis was performed on an Eagle Ford-type reservoir model. The transmissibility, pore volume, pressure, and saturation at an initial time state were used as input features to predict final pressure and saturations. Prediction accuracy of 95% was obtained by hyperparameter tuning off the G-CNN architecture. By compressing the simulation state size and learning the time-dependent reservoir dynamics on this compressed representation, reservoir simulations can be emulated by a graph neural network which uses significantly less computation and memory. The G-CNN model can be used on any computational grid because it preserves the structure of the physics. The G-CNN model is trained by mapping an initial time state to a future prediction time state; consequently, the model can be used for generalizing predictions in any grid sizes and time steps while maintaining accuracy. The result is a computationally and memory efficient neural network that can be iterated and queried to reproduce a reservoir simulation. This novel methodology combines field-scale physics-based reservoir modeling and DNN for reservoir simulations. The new reservoir simulation workflow, based on the G-CNN model, maps the time state predictions in a resampled grid, reducing computational time and memory. The new methodology presents a general method for compressing reservoir simulations, assisting in fast and accurate production forecasting.

Capability of underground carbon dioxide storage to confine and sustain injected CO2 for a long period of time is the main concern for geologic CO2 sequestration. If a leakage from a geological CO2 sequestration site occurs, it is crucial to find the approximate amount and the location of the leak, in a timely manner, in order to implement proper remediation activities. An overwhelming majority of research and development for storage site monitoring has been concentrated on atmospheric, surface or near surface monitoring of the sequestered CO2 . This study aims to monitor the integrity of CO2 storage at the reservoir level. This work proposes developing in-situ CO2 Monitoring and Verification technology based on the implementation of Permanent Down-hole Gauges (PDG) or “Smart Wells” along with Artificial Intelligence and Data Mining (AI&DM). The technology attempts to identify the characteristics of the CO2 leakage by de-convolving the pressure signals collected from Permanent Down-hole Gauges (PDG). Citronelle field, a saline aquifer reservoir, located in the U.S. was considered as the basis for this study. A reservoir simulation model for CO2 sequestration in the Citronelle field was developed and history matched. PDGs were installed, and therefore were considered in the numerical model, at the injection well and an observation well. Upon completion of the history matching process, high frequency pressure data from PDGs were generated using the history matched numerical model using different CO2 leakage scenarios. Since pressure signal behaviors were too complicated to de-convolute using any existing mathematical formulations, a Machine Learning-based technology was introduced for this purpose. An Intelligent Leakage Detection System (ILDS) was developed as the result of this effort using the machine learning and pattern recognition technologies. The ILDS is able to detect leakage characteristics in a short period of time (less than a day from its occurrence) demonstrating the capability of the system in quantifying leakage characteristics subject to complex rate behaviors. The performance of ILDS is examined under different conditions such as multiple well leakages, cap rock leakage, availability of an additional monitoring well, presence of pressure drift and noise in the pressure sensor and uncertainty in the reservoir model.

We describe a novel approach for up-scaling of geological models of heterogeneous reservoirs. The method, which is based on the use of wavelet transformations, incorporates all the relevant data on the permeability, porosity, and other important properties of a reservoir at all the important scales. It generates a nonuniform computational grid which preserves its resolved structure in the regions around the wells and in the high-permeability zones, but is scaled up in the rest of the geological model. To demonstrate the accuracy and efficiency of the method, we apply it to the study of three important problems, namely, the pressure transient analysis, unstable miscible displacements, and two-phase flows. In the study of multiphase flows, we apply a new and robust front detection method. The numerical dispersion is eliminated by a high-order total variation diminishing (TVD) method which yields results that are as accurate as those obtained by the dual-mesh methods, but with a greatly-reduced computational cost. The speed-up in the computations is up to three orders of magnitude. 1. Introduction Computer simulation of unstable miscible displacements and multiphase flows in petroleum reservoirs involves intensive computations1. Advances in measurement and estimation techniques, together with considerable progress in the development of methods for characterization of petroleum reservoirs, can provide significant amounts of data on various properties of the reservoirs. Using such data, modern geostatistical techniques generate highly resolved geological models of heterogeneous petroleum reservoirs. To carry out reservoir simulations, the governing flow and transport equations are discretized, and the flow domain is divided into grid blocks which are characterized by the associated rock properties. Due to computational limitations (mainly computation time) it is not feasible to carry out multiphase flow simulations with a computational grid that corresponds to the highly resolved geological model. It is, therefore, necessary to up-scale the properties of the grid blocks of the geological model in order to develop a coarsened grid that can be used in reservoir simulation with a reasonable amount of computation time. Up-scaling also plays a critical role in reservoir planning and management. In the past, it was common to forecast production based on reservoir simulations that used deterministic models of reservoirs - those that did not take into account the effect of the uncertainties in the distributions of the reservoirs' properties. However, deterministic models yield predictions that may be more harmful to reservoir planning and management than not having a model at all, since the appraisal of the potential production of a petroleum field must take into account the effect of the uncertainties in the properties of the field. Under such conditions, production forecast obtained by a stochastic model of a reservoir allows the quantification of the effect of the uncertainties on the reservoir performance by simulation of fluid flow in many possible realizations of the same reservoir, which are necessarily very time consuming. To reduce the simulation time, up scaling of the geological model is a natural and, perhaps, the only feasible way. For simulation of single-phase flows, the most important parameter is the absolute permeability, for which up-scaling methods are well-established. However, simulation of multiphase flows involves, in addition to computing the equivalent absolute permeabilities of the coarsened blocks, adjustments to the phase flow through the connected blocks of the coarsened grid. To accomplish this many methods have been proposed, including up-scaling techniques that employ pseudo-functions, the so-called dual-mesh approaches, and simpler non-uniform coarsening methods.

Measurement of relative permeability in the laboratory and/or its prediction from models has become an increasingly important subject in reservoir modelling, as reservoir simulation has become the standard method for more accurate forecasting of production and ultimate recovery. However, as is often the case, when the history match in a reservoir simulator is unsatisfactory, the parameter which is most likely to be adjusted is the relative permeability. With developments in measurement and interpretation techniques it is now possible to obtain reliable relative permeability curves. This paper examines current developments in relative permeability measurements from laboratory coreflood experiments, including developments in data reduction and the interpretation of results. It is apparent that classical analytical techniques, such as the Johnson, Bossler and Nauman (JBN) method, cannot describe the majority of unsteady-state displacements, yet are often inconsiderately applied. Valid relative permeability data is only possible if the analytical or numerical model allows for capillarity, viscous instability, wettability and permeability heterogeneity, and can be tuned by carefully measured pressure, flow, and saturation data, and, if possible, non-invasive saturation monitoring. The use of upscaling techniques to bridge the gap between reservoir simulation models and geological scale models poses a great challenge on relative permeability measurements and predictions, at small scale (laminar) levels and for different rock facies. Introduction Relative permeability is used to describe multiphase flow in a porous medium. Such data are important input to many reservoir engineering calculations, providing a basic description of the way in which the phases will move in the reservoir. Definition of the flow process can have a significant effect on the predicted hydrocarbon production rate and duration, and is important in calculating the volume of recoverable hydrocarbon reserves. Although ways to determine relative permeability from measurements made in the field have been proposed, they are fraught with problems and have never been regularly used. The most common method for determining relative permeability has been laboratory special core analysis. Laboratory measurement of representative relative permeability data on a reservoir core-fluid system is a complex task. The experiments are costly and time consuming. Accuracy is limited to the specific core samples and is bounded by narrow saturation limits. A fundamental theoretical approach to modelling multiphase fluid flow in porous rocks is prevented by the complex nature of the problem. Major difficulties arise in mathematically describing flow through a porous system where the lengths, diameters and connectivity of channels are largely unquantifiable, and the complex nature of the hydrocarbon fluids which range from heavy oils to gas condensates with complex thermodynamic behaviours. The extension of Darcy's law to multiple phase flow is, in fact, a heuristic procedure suggested by the analogy with single phase flow. It does not provide an understanding of the physics of multiple phase flow. However, as the law has been substantiated by experiments, it must be assumed that it is at least partly correct and a physical understanding of it has to be sought.

Constructing subsurface models that accurately reproduce geological heterogeneity and their associated uncertainty is critical to many geoscience and engineering applications. For hydrocarbon reservoir modeling and forecasting, for example, spatial variability must be consistent with geological processes, geophysical measurements, and time records of fluid production measurements. Generating such subsurface models can be time-consuming; conditioning them to different types of measurements is even more computationally intensive and technically challenging. Using too many free variables can cause overfitting of the data, thereby decreasing the predictive ability of the model; high dimensionality also slows convergence during history matching of fluid production measurements. To contend with these problems, we introduce here a new machine learning approach, referred to as the stochastic pix2pix method, which parameterizes high-dimensional, stochastic reservoir models into low-dimensional Gaussian random variables in latent space. Many of the world’s significant hydrocarbon fields originate from fluvial or turbidite deposits, with sedimentary processes and spatial distribution of rock facies significantly influencing their flow behavior. Rule- or object-based methods are commonly used to model geostatistically these types of reservoirs. Here, we introduce a new and efficient machine learning-based reservoir modeling workflow capable of generating 2D fluvial reservoir models that account for the available field data and the geometries of different facies. By constraining subsurface model realizations to available geophysical and petrophysical interpretations, multiphysics inversion can be greatly accelerated, thereby requiring only production history matching. Although our models are 2D, an extension to 3D can be readily implemented through zone-by-zone (i.e., reservoir unit) modeling and conditioning. The proposed method and workflow also partially solve the common problem of machine learning methods, wherein mapping low- to high-resolution images often yield reduced spatial variability. The proposed method is an extension of conditional generative adversarial networks, in which we incorporate a novel penalty term into the loss function in order to generate various realizations honoring the same conditional data, such as structural interpretation from seismic data, and borehole measurements at key well locations. We “train” the generative model on geologically realistic, multivariate spatial models generated with a rule-based fluvial reservoir simulator. Each high-resolution training image includes five lithofacies with distinct petrophysical property trends. To evaluate the performance of the proposed method, visual inspection, indicator variograms and multiple point density function are applied to gauge how well the realizations reproduce existing patterns in the true model. A new metric, the mean categorical error, is proposed to quantify how well the realizations match the conditioning data. The proposed method correctly reproduces patterns even when the conditioning data are significantly different from those in the training set. Likewise, the method can perform continuous model modifications, meaning that the machine learning procedure effectively reproduces the migration rule of a meandering (fluvial) system.

Probabilistic production forecasting at Tengiz is largely driven by reservoir uncertainty. Reservoir uncertainty is most effectively synthesized and quantified through simulation modeling. Early in the construction of a new Tengiz dynamic model, fundamental reservoir uncertainties were identified and evaluated. This allowed for model ‘building blocks’ to be developed with different characterizations to encompass key uncertainties. Key uncertainties, which can significantly impact future production under primary depletion and sour gas injection, have been described. These include typical uncertainties such as porosity, irreducible water saturation, hydrocarbon fluid properties, oil-water contact levels, rock compressibility, geologic baffles, and relative permeability. Unique uncertainties specific to Tengiz include geometry and density of the natural fractures, and reservoir heterogeneity. Considerable production history and a large reservoir surveillance database provided input for rigorously characterizing and subsequently validating the range of each uncertainty. After ranges were established, appropriate model realizations were created. A wide range of reservoir models were obtained by selecting combinations of high/mid/low realizations for each uncertainty. Using experimental design (ED), reservoir simulations were conducted to test uncertainty ranges against field history. A quantitative history match and statistical analysis were developed to objectively judge the appropriateness of uncertainty values. Uncertainties with the largest overall impact on the history match are: fracture density, platform horizontal permeability, compressibility, and platform heterogeneity. This case study demonstrates how analysis of reservoir uncertainties can be: (1) captured in static and dynamic reservoir models and (2) validated through ED and quantitative history matching. This study employs state-of-the-art technologies to evaluate model uncertainties of a giant carbonate reservoir undergoing both depletion and miscible gas drives. The range of reservoir models subsequently developed will be of great value in creating robust probabilistic reservoir forecasts to optimize field operation and future development.

Development of a reservoir simulation model involves collection and analysis of an enormous amount of reservoir and well data for initial input. Computer graphics is the most effective tool for geologists and engineers to visualize, verify, and analyze the input data. After each simulation run, simulation engineers then have to depend heavily on computer graphics to analyze an even more intimidating amount of simulation results data. This is especially true at Saudi Aramco because of the large simulation models used to simulate the world's largest hydrocarbon reservoirs. Saudi Aramco's EXPEC Computer Center supports a wide spectrum of computer graphics applications to aid the exploration and engineering staffs through the entire cycle of reservoir characterization. The graphics applications help during the pre-simulation model data analysis as well as in the post-simulation results analysis. Larger and more accurate simulation models are currently being developed In Saudi Aramco. Computer technology (both software and hardware) needs to advance continually such that computer graphics can be utilized as an effective and efficient engineering tool by reservoir simulation professionals. This paper addresses Saudi Aramco's experience in dealing with very large simulation models and using computer graphics as an engineering tool. The program efficiency, device dependence, and data management problems will be discussed. The computer speed, data storage and access methods, data transmission speed, and graphics devices will also be detailed.

The project objective was to detail better ways to assess and exploit intelligent oil and gas field information through improved modeling, sensor technology, and process control to increase ultimate recovery of domestic hydrocarbons. To meet this objective we investigated the use of permanent downhole sensors systems (Smart Wells) whose data is fed real-time into computational reservoir models that are integrated with optimized production control systems. The project utilized a three-pronged approach (1) a value of information analysis to address the economic advantages, (2) reservoir simulation modeling and control optimization to prove the capability, and (3) evaluation of new generation sensor packaging to survive the borehole environment for long periods of time. The Value of Information (VOI) decision tree method was developed and used to assess the economic advantage of using the proposed technology; the VOI demonstrated the increased subsurface resolution through additional sensor data. Our findings show that the VOI studies are a practical means of ascertaining the value associated with a technology, in this case application of sensors to production. The procedure acknowledges the uncertainty in predictions but nevertheless assigns monetary value to the predictions. The best aspect of the procedure is that it builds consensus within interdisciplinary teams The reservoir simulation and modeling aspect of the project was developed to show the capability of exploiting sensor information both for reservoir characterization and to optimize control of the production system. Our findings indicate history matching is improved as more information is added to the objective function, clearly indicating that sensor information can help in reducing the uncertainty associated with reservoir characterization. Additional findings and approaches used are described in detail within the report. The next generation sensors aspect of the project evaluated sensors and packaging survivability issues. Our findings indicate that packaging represents the most significant technical challenge associated with application of sensors in the downhole environment for long periods (5+ years) of time. These issues are described in detail within the report. The impact of successful reservoir monitoring programs and coincident improved reservoir management is measured by the production of additional oil and gas volumes from existing reservoirs, revitalization of nearly depleted reservoirs, possible re-establishment of already abandoned reservoirs, and improved economics for all cases. Smart Well monitoring provides the means to understand how a reservoir process is developing and to provide active reservoir management. At the same time it also provides data for developing high-fidelity simulation models. This work has been a joint effort with Sandia National Laboratories and UT-Austin's Bureau of Economic Geology, Department of Petroleum and Geosystems Engineering, and the Institute of Computational and Engineering Mathematics.

It is well-known that history matching of reservoir models with production measurements is an ill-posed problem, e.g. different choices for the history matching parameters may lead to equally good history matches. We analyzed this problem using the system-theoretical concept of structural identifiability. This allows us to analytically calculate a so-called information matrix. From the information matrix we can determine an identifiable parameterization with a significantly reduced number of parameters. We apply structural identifiability analysis to single-phase reservoir simulation models and obtain identifiable parameterizations. Next, we use the parameterization in minimizing an objective function that is defined as the mismatch between pressure measurements and model outputs. We also apply the structural identifiability analysis to an object-based parameterization describing channels and barriers in the reservoir. We use the iterative procedure to determine for reservoir models with 2025 grid block permeability values a structurally identifiable parameterization of only 13 identifiable parameters. Next, we demonstrate that the parameterization leads to perfect history matches without the use of a prior model in the objective function. We also demonstrate the use of the identifiable object-based parameterization, leading to geologically more realistic history matches.