This work presents PyFastWell, a fast, integrated modelling framework designed to evaluate geothermal doublet performance by semi analytically coupling well inflow modelling, reservoir flow behaviour, and techno economic analysis. The approach builds on the Analytical Element Model (AEM) of Egberts and Fokker, extended here to incorporate temperature dependent viscosity and three dimensional well geometries. The model rapidly computes injectivity, productivity, pressure interactions, and thermal breakthrough risk. The breakthrough risk is evaluated using a cold front tracking module that solves flow aligned heat transport. The tool connects reservoir architecture, well design and techno economic key performance indicators, thus enabling efficient exploration of well configurations. PyFastWell integrates frictional and thermal wellbore losses, thermosyphon effects, and full cash flow calculations to estimate Levelized Cost of Energy (LCOE), Net Present Value (NPV), and Coefficient of Performance (COP). Validation against analytical solutions for vertical, deviated, and horizontal wells demonstrates good agreement across a wide range of geometries, except where published correlations are shown to be erroneous. A stochastic case study for the Rotliegendes reservoir near Utrecht illustrates the substantial performance gains achievable with sub horizontal well designs. The results highlight PyFastWell’s suitability for rapid scenario screening and optimization under geological uncertainty, offering computation times orders of magnitude faster than full numerical simulators. This makes the framework particularly valuable in early stage geothermal development, where many conceptual well architectures must be evaluated.

This paper presents a closed-loop workflow for short-term optimization of reservoir management powered by offshore wind energy. Motivated by the need to reduce CO2 emissions in the Norwegian oil and gas sector, the workflow integrates wind power forecasts into optimization of daily well control while adhering to a long-term production strategy optimized for economic output. The workflow utilizes coarsened reservoir models calibrated through ensemble-based data assimilation to minimize computational costs. A realistic benchmark model, Drogon, is used to demonstrate the methodology. The workflow dynamically adjusts well rates to align power demand with wind availability, minimizing reliance on gas turbines and thereby reducing emissions. The numerical experiment demonstrates the potential of the workflow by significantly reducing short-term emissions without compromising the NPV.

This paper addresses the challenge of incorporating offshore wind power into reservoir management. Traditionally, oil and gas production is powered by gas turbines. While stable, gas turbines are a major source of CO2 emissions. In contrast, wind power produces power with minimal emissions. However, due to its high variability and uncertainty, including it in the optimization of operational strategies over extended periods can be challenging. In this paper, the optimization of production strategies over an ensemble of realistic wind power series is investigated. The ensemble is generated by a mathematical model consisting of an autoregressive model with a seasonal trend. The model is conditioned on relevant wind speed data from the North Sea with Bayesian inference. The wind speed data is selected from the open-access NORA10EI dataset. The methodology developed in this paper is applied to a multi-objective optimization problem, focusing on studying the tradeoff between profit and emissions. A benchmark test reservoir model and a detailed CO2 emissions calculator are employed. In this scenario, wind power is combined with traditional gas power, and all results are compared with a reference where only gas power is used. The experiment indicates that it is not possible to reduce emissions by 40% without the use of wind power.

Model -based reservoir management workflows rely on the ability to generate predictions for large numbers of model and decision scenarios. When suitable simulators or models are not available or cannot be evaluated in a sufficiently short time frame, surrogate modeling techniques can be used instead. In the first part of this paper, we describe extensions of a recently developed open -source framework for creating and training flow network surrogate models, called FlowNet. In particular, we discuss functionality to reproduce historical well rates for wells with arbitrary trajectories, multiple perforated sections, and changing well type or injection phase, as one may encounter in large and complex fields with a long history. Furthermore, we discuss strategies for the placement of additional network nodes in the presence of flow barriers. Despite their flexibility and speed, the applicability of flow network models is limited to phenomena that can be simulated with available numerical simulators. Prediction of poorly understood physics, such as reservoir souring, may require a more data -driven approach. We discuss an extension of the FlowNet framework with a machine learning (ML) proxy for the purpose of generating predictions of H2S production rates. The combined data -physics proxy is trained on historical liquid volume rates, seawater fractions, and H2S production data from a real North Sea oil and gas field, and is then used to generate predictions of H2S production. Several experiments are presented in which the data source, data type, and length of the history are varied. Results indicate that, given a sufficient number of training data, FlowNet is able to produce reliable predictions of conventional oilfield quantities. An experiment performed with the ML proxy suggests that, at least for some production wells, useful predictions of H2S production can be obtained much faster and at much lower computational cost and complexity than would be possible with high-fidelity models. Finally, we discuss some of the current limitations of the approach and options to address them.

In this work, numerical optimization based on stochastic gradient methods is used to assist geothermal operators in finding improved field development strategies that are robust to accounted geological uncertainties. Well types, production rate targets and well locations are optimized to maximize the economics of low-enthalpy heat recovery in a real-life case with stacked reservoir formations. Significant improvements are obtained with respect to the strategy designed by engineers. Imposing fault stability constraints impacts significantly the optimal configurations, with coordinated well rates and placement playing a key role to boost efficiency of geothermal production while keeping stress change effects to acceptable limits.

Underground heat storage is an important element in accelerating the energy transition. It can significantly contribute to CO2 emission reduction and cost savings since it is one of the cheapest forms of energy storage and it enables the seasonal storage of large energy surpluses from sustainable sources, e.g. wind, sun, geothermal. Numerical models are used for the prediction of thermal behavior important in establishing the high efficiency of the high temperature aquifer thermal energy storage (HT-ATES) systems. However, the lack of exact knowledge of the subsurface conditions introduces modeling uncertainty. It is therefore important to employ approaches that reduce subsurface uncertainty. History matching is a methodology where the numerical models are updated to match historical observations that will in turn not only increase understanding of the subsurface but also improve accuracy of the model predictability of future behavior. In this research, models of the first large-scale operational HT-ATES system in Middenmeer, the Netherlands, were used to evaluate the thermal evolution in the storage aquifer and the over- and underburden clay layers. The HT-ATES system, consisting of a hot and warm well, with a monitoring well inbetween, became operational in the summer of 2021. The extensive monitoring program implemented for the first few operational years provided an opportunity to study the performance of such a system from an environmental and operational point of view. A state-of-the-art assisted history matching approach was applied to the first storage cycle, using a coupling between history matching software and the thermal flow simulator. This approach was compared to a more traditional single-model manual history matching method. Rock properties of the aquifer and over- and underburden layers were updated in the randomly generated prior ensemble of models to fit the simulated temperature evolution measured down the monitoring well with the distributed temperature sensing (DTS) data. The observations gathered during the second year of operations were used to validate the accuracy of the prediction capabilities of the updated models. The obtained results indicate the value of history matching to improve understanding of the subsurface conditions for HT-ATES systems and obtain models with better predictability of the future behavior of heat in the storage reservoir and overburden formations. Such improved models are instrumental in providing engineers with a better quantitative grip on the environmentally responsible storage potential and heat deliverability of the target storage site, which is important to achieve cost-effective site-specific design (e.g. number of wells, well placement) and performing operational strategies (e.g. injection/production rates and temperatures) for new HT-ATES systems. Moreover, the benefits of the assisted history matching approach over manual method are highlighted and both approaches are validated where the assisted history matching method produced more accurate predictions than the manual approach.

Abstract Field development planning activities involve decisions that entail multi-billion-dollar investments. Making right design choices is crucial to the techno-economic success of the project. In this paper we demonstrate how state-of-the-art numerical model-based optimization techniques can support asset teams in this complex decision-making process. Optimization of real-life field development cases can be computationally very demanding due to the cost of running large amounts of large-scale reservoir simulations, the large number of variables to be optimized and the necessity of accounting for uncertainties, among other reasons. In this work we employ TNO’s EVEReST optimization technology leveraging the StoSAG stochastic gradient-based method to achieve optimized solutions in computationally efficient manner. Besides its computational attractiveness, the StoSAG method also renders the optimization framework flexible to be customized to any specific optimization problem, e.g., optimization of any type of field development decisions, coupling to any industry-standard reservoir simulators and handling any type of objective and constraint functions. The optimization framework has been used to optimize the expansion of the development plan of a large field in the Middle East, in particular to support decisions concerning the drilling of (up to) 38 new wells and the upgrading of surface facilities to accommodate incremental production. The challenge is posed by the field being comprised of multiple carbonate reservoirs for which multiple types of decisions need to be optimized, often simultaneously i.e. well target locations, trajectory design features, number and type of wells and their distribution across the different reservoirs as well as the drilling sequence. An economic objective function was used with the operational constraints per reservoir accounted for within the framework. Optimization found non-trivial optimal solutions resulting in significant improvements in the economic objective. The optimal strategy revealed improved distribution of wells (and types) among the reservoirs, more suitable drilling order and superior well locations and trajectories compared to the initial strategy. Optimization found well locations and trajectories taking into account complex local well interaction in already densely populated reservoirs with wells. The optimized solutions were benchmarked against the development plan designed by the asset team, and the comparison of strategies confirmed the added value of numerical optimization as a tool to expedite the search of improved development strategies. The nature of the employed optimization method allowed optimized solutions to be achieved with a reduced number of reservoir simulations, which was crucial for the success of this study due to the time-consuming reservoir simulations involved. This showcases the computational advantage of the optimization method and highlights EVEReST as an enabler technology for optimization under uncertainty, where an ensemble of large-scale models is to be considered and the number of required reservoir simulations tends to be even larger.

Offshore field development activities are commonly constrained by the capacity of production facilities available at the platform. It is a challenge for practitioners to find the best development and operational strategies to maximize field production in the presence of such constraints. Additional difficulties are raised when the often large geological uncertainties inherent to field development activities need to be accounted for throughout the search for the best strategy. In this work we present how computer-assisted optimization can help practitioners tackle these challenges. In this work, we focus on the particular problem of selecting the optimal subset(s) of wells to be subject to certain operational actions taking place throughout the field production life-cycle. We leverage robust and efficient field development optimization framework (EVEReST) based on stochastic gradients and its flexibility for customization to new types of decisions. We extend the mathematical parametrization based on a single set of well priorities, so far used to optimize time-static drilling order and well selection optimization. We apply it to multiple sets of well priorities which facilitate the formulation of the large combinatorial time-dynamic well subset selection optimization problem in terms of continuous control variables more suitable for gradient-based optimization techniques. The extended method is applied to two different real-life field case studies based on an offshore heavy oil field. In the first case, we use the well priorities to determine how to optimally select and swap the 14 producers (out of a total of 18) to be put on stream to satisfy constraints on the available variable speed drivers (VSDs) to provide power to the ESP pumps required to operate the producers. In the second case study, we tackle the problem of converting producers into polymer injectors for EOR purposes. Firstly, simulation scenarios indicate that such conversion has a positive impact on the overall project business case. However, deciding which 2 wells (out of a total of 5) to convert into polymer injectors, combined with when and how much polymer to inject while respecting the limits of available capacity of polymer injection facilities, leads to a complex optimization problem. In both case studies, optimization found improved and non-trivial strategies, resulting in significant increases in the economic objective function (+107 million USD and +318 million USD in terms of NPV) and providing new insights to asset engineers into the operations of the field. This showcases the strengths of the EVEReST framework to tackle real-life optimization problems accounting for complex constraints and uncertainties which are critical to deliver solutions of practical value to the asset teams.

Two major CCS projects, PORTHOS and ARAMIS, in The Netherlands are progressing towards final investment decision. CO2 captured from industrial clusters connected to a pipeline network will feed CO2 into multiple offshore depleted gas reservoirs with multiple wells in the Dutch sector of the North Sea.Feasibility studies have matured the transport and storage system and demonstrated the challenges of injecting cold CO2 into hot deep and low pressure (depleted) reservoirs. CO2 properties change dramatically over small changes in pressure and temperature in the reservoir, well and pipeline exacerbated by Joule-Thomson cooling. Undesired effects in the near wellbore zone could be hydrate formation diminishing injectivity, geomechanical effects induced by thermal stresses leading to fracturing causing an increase in injectivity or possible threats to well integrity. In the far field there may be effects on fault stability (depleted gas fields are often fault-bounded). Understanding the physics, the links between transport and storage, as well as the uncertainties are required when assessing safety of operations. It is clear that coupled-simulations involving fluid flow, heat transport, geomechanical effects in pipelines and wells and porous media are necessary.Operating such a complex multi-element system with complex behavior and honoring uncertainty in the various elements requires a clear workflow and a modelling strategy and toolbox, i.e. an integrated CCS model chain (ICCSMC). Such a toolkit should provide all relevant models honoring their links and uncertainties (e.g. through a large model ensemble) to support storage operations and strategic CCS operator decisions. Here we present a schematic/workflow concept that represents this ICCSMC and show how its elements can be selected to drive a model chain to obtain from given input outcomes that support specific decisions. Four examples are shown for the processes of optimisation, data assimilation, conformance assessment and a-priori conformance assessment for monitoring design.

Summary In this study, an ensemble-based optimization framework has been applied to optimize CO2 injection strategies in a case study representative of storage in depleted gas reservoirs. An optimization problem was formulated for a realistic synthetic case study aimed at investigating the potential value of optimization in the context of CO2 storage. Time-varying yearly CO2 injection rates of multiple injection wells were optimized to maximize the net present value (NPV) of the CO2 storage activities while honoring geomechanical constraints associated with fault stability based on the shear capacity utilization indicator. The results of multiple optimization experiments show that when the constraints are imposed the CO2 injection allocation is more balanced across the injectors and over time. This leads to slightly lower NPV for almost the same amount of CO2 injected in optimized case, but it allows us to identify safe injection strategies that still achieve the storage objectives. These results confirm the potential of numerical optimization as a tool to assist CO2 storage practitioners to design safe injection plans.

Summary Typically, subsurface knowledge and operational aspects are key for the success of field-development strategy, well planning, and reservoir-management decisions. In general, reservoir models are used to: 1. quantify subsurface understanding, 2. obtain quality prediction, and 3. support decision making. In practice, it is challenging to build or calibrate reservoir models that preserve enough physical and operational representation to the degree at which quality decisions can be obtained. Therefore, it is necessary to identify the interlink between subsurface understanding and decision quality. The interlink is basically a collection of few subsurface and operational elements that have significant impact to decision – This interlink is referred to as the value of (subsurface and operational) learning. This paper demonstrates the use of an ensemble-based workflow to: 1. identify value of learning, 2. evaluate value of learning with a variety of decisions, and 3. access the consequences of value of learning. In this work, we used EVEREST, a technology for optimization under uncertainty co-owned by TNO and Equinor, to quantify the impact of both geological and operational uncertainties (e.g. drilling time, production time, rig arrival and departure availability) to decision quality. We derived value of learning from the computationally efficient and attractive approximate gradient (StoSAG method) used in EVEREST. The approximate gradient, which here serves as a sensitivity metric, is used to rank the influence of geological and operational parameters to the decision. The key subsurface and operational elements are captured and evaluated for a robust decision making. The proposed workflow is demonstrated with a synthetic but realistic REEK model. The paper addresses on how value of learning could be used in practice with the potential benefits for decision making. The results showed that the interlink between key parameters and key actions are crucial for robust decision making from which the consequences and optimization scenarios are evaluated systematically. In addition, the value of learning helps practitioner access relevant key information that connects key uncertainty to the key decision. The proposed method leads to decision maturation and support system that build the connection between subsurface knowledge, operational aspects, and decision making.

Summary Planning and managing operations of subsurface reservoir assets in terms of conformance is crucial for responsible CO2 storage. One important component of conformance management is the monitoring of the reservoir dynamics in response to the implemented operational strategies, particularly for early detection of deviations from intended behavior (i.e., non-conformance). In recent work, we have introduced a model-based quantitative workflow to objectively assess the usefulness of monitoring for conformance verification in CO2 storage and shown how to use state-of-the-art supervised learning techniques to achieve a more practical workflow. In the present work, we investigate the use of a semi-supervised anomaly detection approach based on auto-encoder neural networks as an alternative to circumvent limitations of the supervised classification approaches explored so far. The results of our case study of a real storage aquifer show that auto-encoders trained on simulated time-lapse seismic data from (only) conformance scenarios can be used to accurately detect scenarios where the migration of CO2 deviates from the desired range of behaviors. These promising results confirm that the proposed approach can be used to derive efficient conformance classification workflows without an explicit finite dataset representing non-conformance to be defined in advance.

Summary Improving the layout of wind farms is one of the keys to increasing the cost-efficiency of offshore wind projects, which is paramount for the success and acceleration of a sustainable energy transition in our societies. Because field development planning and wind turbine placement share many similarities in terms of the decision-making context, we seek opportunities to leverage the know-how from subsurface field development practices to achieve better farm layout designs in offshore wind projects. In this paper, we present how stochastic optimization technology originally conceived to assist oil and gas practitioners in finding optimal field development strategies can be transferred to support decisions in offshore wind farm developments. We particularly focus on comparing different ways of parametrizing mathematically the wind farm layout. We illustrate the use of our framework in realistic case studies, showing that energy production can indeed be increased by optimizing the turbine placement while respecting all hard constraints imposed on the layout design. Finally, we analyze the results obtained to identify the most effective parametrization approach with respect to the shape of the wind farm license area and the presence of complex geometrical constraints.

The scarcity of land near energy demand poses the challenge of designing multi-functional solar parks in terms of land use in some countries. This requires solutions accounting for multiple conflicting objectives, e.g., power generation and multi-functional use of the land (agricultural, construction, ecological). Moreover, the performance of solar park projects in terms of these criteria is subject to uncertainties, e.g., meteorological aspects impacted by climate change, electricity prices, grid infrastructure availability. In this work we present a framework for multi-objective optimization under uncertainty to aid in the development of smart solar park configurations accounting for multi-purpose land use. A solar park simulator and a techno-economic model are combined to evaluate key performance indicators serving as objective functions. Meteorological uncertainty throughout the park lifetime is characterized through an ensemble of scenarios generated based on the variability of historical data, instead of the current practice of assessing the performance of solar park using a deterministic profile of an average meteorological year. The developed workflow is demonstrated through a case study where power yield and agricultural land use are two conflicting objectives being optimized with the orientation, tilt angle, spacing and height of the modules as the optimization variables. A series of optimization experiments with varying importance weights between the objectives is performed. Obtained solutions show solar park designs which double the available farming area without compromising the levelized cost of energy. These results showcase the value generated through an integrated framework for multi-objective optimization under uncertainty leading to optimized solar parks.

Summary Advances in applications of reservoir management workflows have shown the value of closed-loop optimization in leveraging the learning from measurements gathered during operations to improve subsequent operational decisions. Closed-loop workflows rely on the combination of optimization and history matching procedures, which both can involve a very large number of reservoir simulations when employing ensemble-based methods for uncertainty quantification. This renders such approach unfeasible in many real-life applications which involve large-scale models. The problem is amplified in more advanced workflows where the closed-loop calculations must be repeated several times (e.g., value of information approaches to assess and optimize the effectiveness of monitoring strategies). Recent developments in direct forecasting techniques such as data-space inversion (DSI) have shown promising results to alleviate the computational burden associated with the generation of ensemble of simulated forecasts conditioned to measurement data and their use in optimization workflows. In this work, we present an implementation of the DSI framework using the ES-MDA method available within a mature open-source data assimilation tool suitable for large-scale reservoir applications. The developed workflow utilizes machine learning techniques to better handle the presence of non-linearities typical of real-life applications (e.g., well shut-ins) and also accounts for the variability of well controls to enable the use of the forecasts for well control optimization purposes. We demonstrate the workflow with two realistic synthetic case studies. In the first case, we illustrate the forecasting of the extent of the plume of CO₂ injected in an aquifer reservoir. In the second example we couple the developed DSI framework to an ensemble-based optimization framework to optimize water injection rates in an oil-water reservoir based on the forecasts of cumulative production conditioned to production data. The outcome achieved with DSI is verified against the response of synthetic truth models to assess the validity of the approach. The results obtained confirm the potential of DSI as a suitable technique to enable the acceleration of closed-loop and monitoring design optimization workflows. Moreover, the coupling with an ensemble-based optimization framework opens up opportunities to extend its use to optimize other types of reservoir management and field development decisions.

Summary Flow networks, in which flow paths are approximated by one-dimensional flow tubes, have recently appeared as a potentially powerful data-physics subsurface reservoir surrogate modelling technique. Such models are fast, because the number of grid cells is considerably reduced when compared to conventional numerical reservoir model grids. Flow network models can be generated and trained directly from data rather than constructed through reduction of high-fidelity models and can be simulated using existing industry-standard reservoir simulators. The first objective of this paper is to summarize recent extensions of an open-source framework for creating and training flow networks, called FlowNet, and to demonstrate its application to a complex oil field case. Despite their flexibility, the application of flow networks will be limited to physics that can be simulated with the available simulators. Prediction of phenomena with poorly understood underlying physics, chemistry, or, as in the case of reservoir souring, microbial ecology, may require a more data-driven approach. The second objective of this paper is therefore to investigate the usefulness of an extension of the FlowNet methodology with a machine learning proxy that can be used to produce predictions of H2S in the absence of a physics-based simulator module. The proxy is trained on historical liquid volume rates, seawater fractions, and H2S production data from a complex producing field, and then used to generate predictions of H2S production using FlowNet-based predictions of these same features as input. We introduce the main characteristics of the field case, including a brief review of current understanding of reservoir souring taking place in the field. Several experiments are presented in which the source, type, and length of the training data time series are varied. Results indicate that, given a sufficient number of training data points, FlowNet is able to produce reliable predictions of conventional oil field quantities. The experiments performed with the machine learning proxy as an add-on, suggest that, at least for certain classes of production wells, useful predictions of H2S production can be obtained much faster and at much lower computational cost and complexity than would be possible with high-fidelity models. Finally, we discuss some current limitations and options to address them.

Summary The success and growth of geothermal development activities in the Netherlands in the coming years strongly depend on the capability to increase the cost-effectiveness of heat recovery from subsurface reservoirs. Field development optimization can help achieve that goal. In this work we apply state-of-the-art optimization technology to assist geothermal operators in finding improved field development strategies. The computer-assisted framework takes advantage of modern stochastic gradient-based methods that enable it to handle a large number of optimization variables in a computationally efficient manner even when considering an ensemble of model realizations to characterize geological uncertainties. We present results of a recent application of our optimization framework to a real-life case study with stacked reservoir layers. Starting from well configuration provided by the asset operator, we optimize the type of wells (injector or producer) and production rates from the different stacked reservoirs while considering realistic physical and operational constraints and accounting for geological uncertainty with an ensemble of 40 model realizations. Significant improvements in terms of the project economics (+9.6%) and heat production (+5.6%) are obtained, confirming the potential of optimization as a decision support tool to boost the efficiency of geothermal production.

In this work, we propose a workflow for quantifying the value of a candidate monitoring strategy for conformance management decisions. A value of information (VOI) analysis framework is constructed by combining a quantitative conformance verification workflow and an economic model via decision tree analysis. The presented workflow is applied to a synthetic case study based on the Smeaheia storage aquifer, where the uncertainty on the transmissibility of an important fault poses a risk to the conformance of the reservoir pressure behaviour. By incorporating information from candidate monitoring configurations to better constrain the forecasts of the ensemble of model realizations used to characterize the inherent geological uncertainties, an assessment of the time-dependent ability of detecting deviations from conforming behaviour is achieved and followed by an economic evaluation of the considered alternatives for pressure control actions. Using data-driven approximations, the analysis can be repeated for several monitoring configurations (type of measurement, location, measurement error and timing of measurement) accounting for plausible outcomes of the analysed measurements. This allows us to determine the expected value of the storage operation for each monitoring configuration, and for possible actions available to the operator for remediating unwanted storage site development. The results obtained show that the value achieved from monitoring depends on both the timing and information content of the measurements. Direct and indirect (imperfect) observations of the quantity of interest for conformance might not always be sufficient to guarantee perfectly accurate conformance statements and conformance management decisions. This highlights the practical value of addressing decision problems in quantitative terms to support the design of CO2 storage systems by gaining insight into the effectiveness of monitoring and corrective actions.

We present the results of a rock physics driven workflow to analyze and steer a planned CO2 migration campaign, it combines a rock-physical seismic model, reservoir model and Bayesian scenario framework to assess monitoring configurations. The injection of CO2 into the subsurface requires a detailed understanding of the petrophysical properties and rock physical frame of the formation under stress. The underlying assumptions made for the unconsolidated high porosity formation have a significant impact on the expected seismic response. For a shallow aquifer pressure and saturation dependent elastic parameters are derived within a rock physical description. Distinguishing pressure and saturation related changes of the acoustic impedance is subject to the sensitivities of the properties used to derive the underlying seismic P- and S-velocities as well as densities. These uncertainties can induce a non-negligible variability in the footprint of a seismic image of the CO2 plume. The rock physics model is based on a solid frame consisting of quartz and clay and saturated with water and gas. The integrated workflow coupled with dynamic simulations provides a possibility to define and evaluate conformance measures during operation. Within this context the workflow is applied to the extended Svelvik CO2 field laboratory, which can be considered as a big sandbox model with capabilities for up-scaling to larger storage formations. An experimental design assessing the sensitivity of the information present for the site is translated into a scenario-based ensemble of dynamic models. Following, a practical quantitative workflow for a-priori assessment of monitoring strategies in probabilistic conformance verification is demonstrated. The results provide insight into the impact of different aspects of geophysical monitoring configurations (e.g., sparsity, noise levels, detection thresholds and timing) on the conformance verification accuracy.

We propose a quantitative model-based workflow for conformance verification of CO2 storage projects. Bayesian inference is applied to update an ensemble of simulation models that capture prior uncertainty based on mismatches with measured data. Conformance assessments are derived by comparison of updated model predictions with storage permit requirements and confidence criteria. Two examples, one conceptual and one based on a real candidate storage site, are provided in which the quantitative workflow is applied to the a priori assessment of candidate monitoring strategies. The examples illustrate the limitations of pressure monitoring in the presence of realistic subsurface uncertainties, and the potential for cost saving by informed design of geophysical monitoring surveys. Approximate methods are discussed that could make the workflow also applicable for (quasi) real-time conformance monitoring.