Geological Uncertainty Research Articles

Managing geological uncertainty in expensive reservoir simulation optimization

A method to manage geological uncertainty as part of an expensive simulation-based optimization process is presented. When the number of realizations representing the uncertainty is high, the computational cost to optimize the system can be considerable, and often prohibitively, as each forward evaluation is expensive to evaluate. To overcome this limitation, an iterative procedure is developed that selects a subset of realizations, based on a binary nonlinear optimization subproblem, to match the statistical properties of the target function at known sample points. This results in a reduced-order model that is optimized in place of the full system at a much lower computational cost. The result is validated over the ensemble of all realizations giving rise to one new sample point per iteration. The process repeats until the stipulated stopping conditions are met. Demonstration of the proposed method on a publicly available realistic reservoir model with 50 realizations shows that comparable results to full optimization can be obtained but far more efficiently.

Coupled modeling and dynamic characteristics of TBM cutterhead system under uncertain factors

The cutterhead system is the most critical component of a tunnel boring machine (TBM). Because of its complex structure and the influence of many uncertain factors, which not only aggravate its vibration damage, but also make the prediction of its dynamic characteristics and the damage-resistant design very challenging, coupled modeling and the dynamic characteristic analysis of cutterhead system under uncertain factors are of great significance. In this paper, firstly, we considered the influence of geology and cutter position, the geological uncertainty model and the uncertainty model of the cutter position were established for the first time. The combination and transformation of the uncertainty model and the obtained load interval were realized successfully. Then, the load interval was substituted into the dynamic model, the multi-degree-of-freedom coupled dynamic model of the cutterhead system under uncertain factors was obtained. We combined interval theory with the central difference method to solve the dynamic model and obtained the dynamic response boundary. The necessity of considering uncertainty and the effectiveness of this method were verified by comparing the data under uncertain factors with the field test data and simulation data. Finally, we applied our method to an engineering cutterhead system. The results showed that (1) The five-part cutterhead has better vibration characteristics than the integrated cutterhead and two-part cutterhead; (2) When the mass of the center block accounts for 45–46% of the total, the vibration amplitude is the smallest. This provides a very important theoretical reference for structural design and vibration reduction optimization of the cutterhead system.

Applications of Geophysical Logs to Coal Mining—Some Illustrative Examples

Geophysical logs can be used not only for qualitative interpretation such as strata correlation but also for geotechnical assessment through quantitative data analysis. In an emerging digital mining age, such a use of geophysical logs helps to establish reliable geological and geotechnical models, which reduces safety and financial risks due to geological and geotechnical uncertainty for new and existing coal mining projects. This paper presents some examples of geological and geotechnical characterizations from geophysical logs at various coal mines in Australia and India. The applications include rock strength and coal quality estimations, automated lithological/geotechnical interpretation and geophysical strata rating, all based on geophysical logs. These derived parameters could provide input to modelling, control, even ‘digital twin’ generation in a form of geological and geotechnical models as part of the future digital mining. The outcomes can be visualized in 3D space and used for identifying the key geotechnical strata units that are responsible for caving behaviors during longwall mining. This could assist site geologists and planning and production engineers predict and manage mining conditions on an ongoing basis. Both conventional logs such as density, natural gamma and sonic and less common logging data, such as full waveform sonic, televiewer and SIROLOG spectrometric natural gamma logging data are examined for their potential applications. The geotechnical strata classification and rock strengths predicted from the geophysical logs match the laboratory tests, drill core geotechnical strata classification, core photos and the mining condition/behavior observed. These illustrate the usefulness and effectiveness of using geophysical logs for geological and geotechnical characterizations.

Multi-objective optimization under uncertainty of geothermal reservoirs using experimental design-based proxy models

Geothermal energy has a high potential to contribute to a more sustainable energy system if the associated economic risks can be overcome in the design process. The development planning of deep geothermal reservoirs (over 1000 m depth) relies on computer models to forecast and then optimize system design. Optimization is easy where all the objective's (e.g. NPV) optimization parameters and, most importantly, the geology are considered as known, but this is almost always not the case. Where the complex engineering design (e.g. well placement) meets significant geological uncertainty every development option should be tested using an expensive simulation against the range of geological possibilities. The impracticality of simulating so many models results in a limited exploration of geological uncertainties and development options. Consequently, the risk of improper system design cannot be properly assessed. This paper presents an approach to understand the trade-offs in maximizing heat extraction while minimizing energy usage in re-injection for a new geothermal reservoir development while considering the uncertainty from 18 different geological models. Our approach is computationally feasible because we apply multi-objective particle swarm optimization (MOPSO), to an ensemble of response surface models, built using Gaussian process regression (GPR), for each and every geological scenario. MOPSO explores the trade-off surface for the competing objectives using the mean reservoir responses (covering the geological uncertainty). Our results highlight the impact of geological uncertainty on the optimal well placement and show the need to consider geological uncertainties adequately in optimization. The work demonstrates the shortcomings of using only one geological model of a geothermal reservoir and/or a single objective in optimization. We additionally demonstrate the practicalities of using response surface models in this way for geothermal systems. We anticipate that our work raises awareness for the scope of optimization of geothermal reservoir design under geological uncertainty.

Managing Geologic Uncertainty in Pit Shell Optimization Using a Heuristic Algorithm and Stochastic Dominance

Optimizing final pit limits for stochastic models provides access to geologic and economic uncertainty in the pit optimization stages of a mining project. This paper presents an approach for optimizing final pit limits for a highly variable and geologically complex gold deposit. A heuristic pit optimizer is used to manage the effect of geological uncertainty in the resources within a pit shell with multiple uncertainty rated solutions. The uncertainty rated pit shells follow the mean-variance criterion to approximate the efficient frontier for final pit limits. Stochastic dominance rules are then used in a risk management framework to further eliminate sub-optimal solutions along the efficient frontier. This results in a smaller set of final pit shells that could be further analyzed for production scheduling. Additionally, the original solutions are analyzed for changes in the mining limits and two regions are targeted as potential regions for further exploration.

Closed-loop stochastic oilfield optimization for hedging against geologic, development, and operation uncertainty

Development and management of oilfields involve several sources of uncertainty that complicate an already challenging decision-making process. Two main sources of uncertainty are related to geologic description of reservoirs and future development and operation scenarios. While geologic uncertainty has been widely studied and robust optimization methods have been developed to account for it, the uncertainty in future development and operations has not been considered. Traditionally, future development strategies have been included as optimization decision variables. However, in practice, due to unpredictable factors, future plans tend to deviate from the solutions obtained in optimization problems. In a recent publication, we showed that a more prudent and realistic approach toward oilfield optimization is to consider future development plans as uncertain parameters to ensure that the obtained solutions remain robust when future plans change. Here, we develop a closed-loop stochastic field development optimization formulation to account for the uncertainty in geologic description and future development and operation scenarios. The proposed approach optimizes the decision variables for current stage of planning (e.g., well locations and operational settings) while accounting for geologic and future development and operation uncertainties. Geologic uncertainty is represented with several reservoir model realizations, while development uncertainty is incorporated through drilling scenario trees and stochastic well placement. To account for operation uncertainty, future well controls are also described probabilistically. Prior to each development decision-making stage, the reservoir is operated using a closed-loop control approach, including model calibration against dynamic data, subject to geologic and future development and operation uncertainties. After each data assimilation step, a new stochastic optimization is performed to adjust controllable decision variables for the current well configuration (e.g., well rates or BHPs) using the updated models and potentially revised future development scenarios. Using a multistage stochastic optimization workflow, this process is repeated after each decision stage. Several numerical experiments are presented to discuss various aspects of the proposed closed-loop stochastic optimization formulation and to evaluate its robustness.

Using an Integrated Multidimensional Scaling and Clustering Method to Reduce the Number of Scenarios Based on Flow-Unit Models Under Geological Uncertainties

AbstractUnderstanding the role of geological uncertainties on reservoir management decisions requires an ensemble of reservoir models that cover the uncertain space of parameters. However, in most cases, high computation time is needed for the flow simulation step, which can have a negative impact on a suitable assessment of flow behavior. Therefore, one important point is to choose a few scenarios from the ensemble of models while preserving the geological uncertainty range. In this study, we present a statistical solution to select the representative models (RMs) based on a novel scheme of measuring the similarity between 3D flow-unit models. The proposed method includes the integration of multidimensional scaling and cluster analysis (IMC). IMC can be applied to the models before the simulation process to save time and costs. To check the validity of the methodology, numerical simulation and then uncertainty analysis are carried out on the RMs and full set. We create an ensemble of 200 3D flow-unit models through the Latin Hypercube sampling method. The models indicate the geological uncertainty range for properties such as permeability, porosity, and net-to-gross. This method is applied to a synthetic benchmark model named UNISIM-II-D and proves to offer good performance in reducing the number of models so that only 9% of the models in the ensemble (18 selected models from 200 models) can be sufficient for the uncertainty quantification if appropriate similarity measures and clustering methods are used.

Improved methods for determining effective sandstone reservoirs and evaluating hydrocarbon enrichment in petroliferous basins

Geological uncertainty is the greatest risk in oil exploration. Most of the evaluation and risk assessment of exploration projects are characterized by the hydrocarbon enrichment evaluation of the target reservoir to avoid non-effective reservoirs. Based on experiments and detailed analysis of the hydrocarbon accumulation process, we propose a new criterion, namely reservoir hydrocarbon enrichment potential index, expressed as PI, for improved evaluation and assessment. According to the index PI, sandstone reservoirs are classified into four categories: Level IV, PI ≤ 0; Level III, 0 < PI ≤ 0.01; Level II, 0.01 < PI ≤ 0.1; Level I, 0.1 < PI ≤ 1. Level IV reservoirs are non-effective, whereas Level III, Level II, and Level I reservoirs are effective and their hydrocarbon enrichment potentials increase progressively. The recommended strategy is to prioritize the exploration of sandstone reservoirs in the order of Level I > Level II > Level III and avoid Level IV reservoirs. This strategy was applied to the Mesozoic sandstone in western Sichuan Depression, China, to demonstrate the method and workflow. Drilling success rate is positively correlated with the PI of the sandstone reservoir. The results of 153 wells drilled in western Sichuan Depression show the accuracy of the method to be 84.5%. Therefore, this method is useful for quick and effective scientific decisions on oil exploration projects, thus mitigating the risks in oil exploration projects.

Evaluation of cooperative mitigation: captured carbon dioxide for enhanced oil recovery

Robust mitigation options will play significant role in achieving the target of limiting global change to below 1.5 °C above pre-industrial levels by 2100. To support cooperation for mitigation development, we establish a real options-based model to evaluate the rational decisions of exercising the abandon option for carbon capture and storage-enhanced oil recovery (CCS-EOR) projects under oil market and geological uncertainties. Three possible cooperative mechanisms (fixed carbon dioxide (CO2) price, oil-indexed CO2 price, and joint venture contracts) among CO2-EOR stakeholders are evaluated. The results show that the conflicts in profit maximization targets for different stakeholders in cooperative mitigation are to a great extent unable to be avoided. A joint venture business model is preferred in cooperative mitigation as it can effectively weaken such conflicts. And it is more reasonable to provide incentives to the downstream of the CO2-EOR chain than compensating the adoption cost of carbon capture technologies in the upstream. From a global perspective, the inefficient cooperation can be a main barrier that hinders the development of deep-cutting options. Global mitigation strategies should not only focus on promoting technology progress but also the design of innovative business models to balance the benefits among stakeholders. A joint venture business model is recommended in both the developed and developing countries for seizing the early mitigation opportunities.

Confidence-Constrained Support Vector Regression for Geological Surface Uncertainty Modeling

Reconstruction of complex geological surface is widely used in oil and gas exploration, geological modeling, geological structure analysis, and other fields. It is an important basis for data visualization and visual analysis in these fields. The complexity of geological structures, the inaccuracy and sparsity of seismic interpretation data, and the lack of tectonic morphological information can lead to uncertainty in geological surface reconstruction. The existing geological surface uncertainty characterization and uncertain reconstruction methods have a shortcoming in balancing the interpolation error of high-confidence samples and model structure risk. Based on support vector regression (SVR), a method with confidence constraints for uncertainty characterization and the modeling of geological surfaces is proposed in this article. The proposed method minimizes the structural risk by adding a regularization term representing the model complexity, integrates high-confidence samples, such as drilling data, based on confidence constraints, and utilizes well path points by assigning appropriate inequality constraints to the corresponding prediction points. The results based on a real-world fault data set show that the uncertainty envelopes and fault realizations generated by the proposed method are constrained by well observations and well paths, effectively reducing the uncertainty.

INFLUENCE OF THE MAIN FACTORS ON THE CONDITIONS OF FORMATION OF THE ACHIMOV FORMATION

Western Siberia is a unique geological area for testing different approaches of sequential stratigraphy and their impact on deep-sea sediment architecture. There are many different various techniques allow us to reduce geological uncertainty and predict distribution and architecture of deep-water sediments. The architecture of deepwater sediments is influenced by the basin topography and volume of sediment supply. In this paper the main factors and their influence on the deep-water sediments accumulation will be considered. This approach was used for prediction distribution and quality of deep-water sediments in poorly studied areas. Progradation and retrogradation trends, topography of basin floor and shelf`s edge trajectorywere analyzed to determine an effect. Based on empirical estimation, it has been determined that the most important factor is the trajectory of change of shelf`s edge and topography. There was no apparent correlation between the quality of deep-water sediments and the amount of volume sediment supply. All of this data and correlation was used for estimation potential of the achimov formation in poorly studied areas.

An accelerated gradient algorithm for well control optimization

The simulation-assisted optimization of injection and production control settings can be considered as a way for improved management of hydrocarbon reservoirs. Although well control optimization problems are expected to have a relatively smooth landscape, they suffer from having a high number of dimensions. As a result, gradient-based algorithms are typically the preferred choice over derivative-free algorithms. Since the calculation of the exact gradient is not always feasible, recent studies into gradient-based algorithms have focused on ameliorating the accuracy of gradient approximation methods. Less attention has been paid to the framework in which these gradient approximations are utilized. The most common framework is the steepest descent with a backtracking line-search. This study aims to provide an improved framework using an adaptive moment estimation technique. The proposed method is compared to the steepest descent framework in two case studies of increasing complexity. In both frameworks, the gradient is approximated by the simultaneous perturbation stochastic approximation. The first case study is a two-dimensional heterogeneous reservoir model produced under water-flooding. The second case study investigates the injection and production settings of the three-dimensional benchmark case, the Brugge model, whilst incorporating geological uncertainty. The proposed framework showed improvements of up to 91% in convergence speeds and up to 5% in optimal value for the first case study. In addition, the proposed framework showed an improvement of up to 81% in convergence speeds and up to 2% in optimal value for the second case study over the steepest descent framework. For both case studies, the effect of the gradient approximation accuracy (perturbation number) was also investigated. The results indicated that the proposed algorithm is less sensitive to the gradient approximation accuracy than the steepest descent framework. In addition, this study investigated the effect of two popular bound constraint handling techniques in both frameworks. The results indicated that the projection method outperforms the logarithmic transformation method when applied to production optimization problem.

A dynamic stochastic programming approach for open-pit mine planning with geological and commodity price uncertainty

Over the last decade, geological uncertainty and its effects on long-term or strategic mine planning and methods for related risk management have been studied. However, the combined effect of geological and commodity price uncertainty has received relatively less attention in the technical literature. A research experiment that addresses both these sources of uncertainty is presented herein and accounts for their differences. In particular, while the current commodity price is known at the beginning of every new mining period, the geology, including the mineral grades, metal content, material types and so on, remain uncertain, even when additional information becomes available. The proposed method first uses a two-stage model to manage the geological uncertainty that leads to a scenario-independent extraction sequence. Based on different metal production targets, a pool of subsets of mining blocks is also precomputed for every period. Then, a stochastic dynamic programming algorithm is developed and employed to define the best policy in terms of metal production targets to follow, depending on the evolution of the related commodity price. This policy follows the scenario tree of the commodity price, as it is scenario-dependent (price only) with non-anticipativity constraints, which is similar to an operator that adapts to a fluctuating market. This new approach is tested through a case study that reveals the counter-intuitive combined effects of both sources of uncertainty. For instance, based on the previous evolution of the commodity price, the obtained policy suggests adaptations of the metal production target that go against common practices of mining operators.

Fault fictions: systematic biases in the conceptualization of fault-zone architecture

Abstract Mental models are a human's internal representation of the real world and have an important role in the way we understand and reason about uncertainties, explore potential options and make decisions. Mental models have not yet received much attention in geosciences, yet systematic biases can affect any geological investigation: from how the problem is conceived, through selection of appropriate hypotheses and data collection/processing methods, to the conceptualization and communication of results. We draw on findings from cognitive science and system dynamics, with knowledge and experiences of field geology, to consider the limitations and biases presented by mental models in geoscience, and their effect on predictions of the physical properties of faults in particular. We highlight biases specific to geological investigations and propose strategies for debiasing. Doing so will enhance how multiple data sources can be brought together, and minimize controllable geological uncertainty to develop more robust geological models. Critically, there is a need for standardized procedures that guard against biases, permitting data from multiple studies to be combined and communication of assumptions to be made. While we use faults to illustrate potential biases in mental models and the implications of these biases, our findings can be applied across the geosciences.

Distributed Gauss-Newton Optimization with Smooth Local Parameterization for Large-Scale History-Matching Problems

Summary Finding multiple posterior realizations through a reservoir history-matching procedure is of significance for uncertainty quantification, risk analysis, and decision making in the course of reservoir closed-loop management. Previously, an efficient, distributed, optimization algorithm—global linear-regression (GLR) with distributed Gauss-Newton (GLR-DGN)—has been proposed in the literature to iteratively minimize multiple objective functions by performing Gauss-Newton (GN) optimizations concurrently while dynamically sharing information between dispersed regions in a reduced parameter space. However, theoretically, the required number of initial training models should be larger than the number of parameters to guarantee a unique solution for the GLR equation for sensitivity-matrix estimation. This limitation makes the large-scale reservoir history-matching problem with a large amount of parameters almost intractable. We enrich the previous history-matching framework by integrating our recently proposed smooth local parameterization (SLP) and DGN for the sensitivity-matrix calculation. Motivated by the fact that one specific flow response mainly depends on a few influential or local parameters, which can be generally identified by the physical position of the wells (e.g., the parameters in the zone surrounding the wells), which is particularly true for large-scale reservoir models, this paper presents a new integration of subdomain linear-regression (SLR) with DGN, referred to as SLR-DGN. This SLP allows us to independently represent the globally spatial parameter field within low-order parameter subspaces in each subdomain. On the basis of the SLP procedure, only a few training models are required to compute local sensitivity of the response functions using a subdomain linear regression. SLP is a linear transformation with smoothness and differentiability, which makes it particularly compatible with Newton-like gradient-based optimization algorithms. Furthermore, we also introduce an adaptive scheme, named weighting smooth local parameterization (WSLP), in which the minimization algorithm adaptively determines the weighting coefficients and the optimal domain decomposition (DD) correspondingly, to mitigate the negative effects of an inappropriate DD strategy. We support our framework with numerical experiments for a four-variable toy model and a modified version of the sensitivity analysis of the impact of geological uncertainties on production (SAIGUP) model with spatially dependent parameters. Comparisons with previous GLR-DGN have shown that our new framework can generate comparable and even better results with significantly reduced computational cost. This SLP has high scalability, because the number of training models depends primarily on the number of local parameters in each subdomain and not on the dimension of the underlying full-order model. Activating more subdomains results in fewer local parameter patterns and enables us to run fewer training models. For a large-scale case study in this work, to optimize 412 global parameters, SLR-DGN needs only 100 initial-model simulations. In comparison to GLR-DGN where the parameters are defined over the entire domain, the central-processing-unit cost is reduced by a factor of several orders of magnitude, while retaining reasonable accuracy.

Olympus challenge—standardized workflow design for field development plan optimization under uncertainty

The “Olympus challenge” is defined as an open benchmark study on field development optimization under geological uncertainty. This work describes a structured approach to the Olympus challenge. It combines a systematic performance delivery analysis based on multiple reservoir model realizations with optimization strategies including well controls, field development scenarios, and combined strategies. The ambition of this work is to design practical and robust workflows integrating economic, reservoir geology, and delivery performance that can be applied to current real field studies. Probabilistic assessments on economic performance and reservoir opportunities are used for project framing and to define start points for optimization strategies. A sequential optimization strategy is applied to handle discrete control parameters with a time-dependent impact on economic performance over the life cycle of the reservoir. Probability maps are applied to identify reservoir opportunities and a probabilistic well ranking is introduced to investigate the robustness of a well location design. Objective measures for probabilistic evaluations are described and applied for result comparison between an optimized and a reference solution in the presence of multiple realizations. In conclusion, this work provides solution proposals for multiple optimization objectives of the Olympus challenge. Standardized workflow designs deliver manageable and repeatable work steps and give an outlook to automation.

Reducing uncertainty in geologic CO2 sequestration risk assessment by assimilating monitoring data

Geologic CO2 sequestration sites usually have large uncertainty in geological properties, such as uncertainty in permeability and porosity fields. Geological uncertainty leads to significant uncertainty in predicted risk metrics such as CO2 plume extent, CO2/brine leakage rates through wellbores, and impacts to drinking water quality in groundwater aquifers due to CO2/brine leakage, all of which will impact the approach for post injection site care (PISC). Pre-injection risk assessment can be used to quantify the amount of uncertainty in different predicted risk metrics. However, it cannot account for the potential value of monitoring data (e.g., CO2 saturation and pressure measurements) acquired during the operation of CO2 storage. In this study, we demonstrate how uncertainty in predicted risks can be reduced by performing monitoring data assimilation. An ensemble of geological reservoir models, constrained by direct measurements (such as permeability estimates from exploratory wells), are generated by geostatistical conditional simulation. As the monitoring data from the storage site become available, they are assimilated into models using a recently developed data assimilation method, ES-MDA with geometric inflation factors (ES-MDA-GEO). The reservoir models, calibrated through multiple data assimilation iterations, are used to predict future risks and reduction in their uncertainties. The proposed approach for the quantification of uncertainty reduction in risk assessment is demonstrated with two examples: a generalized 3D synthetic case and a synthetic field case based on the Rock Springs Uplift site in southwestern Wyoming.

Automated Approach Optimizes Flow-Control Device Placement in SAGD Completions

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 193364, “Optimization of Placement of Flow-Control Devices Under Geological Uncertainty in Steam-Assisted Gravity Drainage,” by Siavash Nejadi, Stephen M. Hubbard, Roman J. Shor, SPE, Ian D. Gates, SPE, and Jingyi Wang, SPE, University of Calgary, prepared for the 2018 SPE Thermal Well Integrity and Design Symposium, Banff, Alberta, Canada, 27–29 November. The paper has not been peer reviewed. Steam-chamber conformance in steam-assisted gravity drainage (SAGD) influences the efficiency and economic performance of bitumen recovery. Conventional SAGD well-completion designs provide limited control points in long horizontal well pairs, leading to development of nonideal steam chambers. The complete paper presents an automated approach to optimizing placement of flow-control devices (FCDs) in SAGD well-pair completions. The methodology uses a coupled wellbore/reservoir model to simulate both reservoir fluid-flow behavior and detailed wellbore hydraulics. The qualities of the well-completion-design parameters and their effect on production are assessed by calculating the net present value (NPV), which is considered the basis for•optimization. Introduction The Lower Cretaceous McMurray formation hosts the majority of bitumen in the Athabasca oils sands—the largest known resource of bitumen. The formation is composed of large-scale fluvial-estuarine point bars and other laterally accreting channel systems that are highly heterogeneous. The formation has been interpreted as having three stratigraphic subdivisions: a lower continental (fluvial), a middle fluvial-estuarine unit (point-bar dominated), and an upper marginal marine deposit. The repeated erosional cut and fill events within the McMurray have led to nested and multiple stacked structures. Similarly, laterally accreting channel systems, such as point-bar deposits consisting of inclined heterolithic strata of sandwiched sand-siltstone sequences and abandoned mud channels, lead to very complex sedimentary facies relationships in which rock types change both laterally and vertically over very short distances. SAGD completions offer the most promise of producing bitumen resources from Athabasca oil-sands deposits. The SAGD well configuration typically consists of two parallel horizontal wells within a short distance, one above the other. Steam is injected into the upper well and fluids are produced from the lower well. These design schemes provide limited control of steam injection and liquid production along horizontal sections and adversely affect steam-chamber conformance. Operational difficulties, drilling, well completion, and reservoir parameters moderate overall SAGD performance. More specifically, these factors include hydraulic gradients and pressure drop in the tubulars, injectivity and productivity variations along the wellbore from plugging and formation damage, heat exchange, energy loss to bottom water, nonparallel well-pair placement, well undulation, and—most importantly—reservoir heterogeneity and structure. The conventional SAGD well completion needs to be modified to deliver steam efficiently throughout the reservoir interval, improve liquid-production performance, and minimize steam breakthrough.

Reservoir modelling notional CO 2 injection into the Precipice Sandstone and Evergreen Formation in the Surat Basin, Australia

The Lower Jurassic Precipice Sandstone and Evergreen Formation are an important prospective reservoir–seal pair for CO 2 storage in the Surat Basin, Australia. However, there is little seismic and well data to constrain reservoir modelling in the best notional injection area. To test the likely storage performance, three contrasting sector-scale static reservoir models were built to capture the range of geological uncertainty in facies distribution and reservoir properties. These considered sectors of the Surat Basin with different palaeogeographical arrangements. The models were focused on capturing detail at the interface between the top of the Precipice Sandstone (Blocky Sandstone Reservoir: BSR) and the overlying basal portions of the Evergreen Formation (Transition Zone: TZ), a critical area for understanding CO 2 injection. Object modelling was used for the BSR and lower TZ. Stochastic modelling was implemented for the upper TZ and the Ultimate Seal because these zones were less sensitive to facies distributions. Porosity was modelled stochastically, and permeability calculated using porosity–permeability transformation functions. Dynamic simulation showed the TZ has the capacity to arrest CO 2 flow out of the BSR given appropriate CO 2 injection conditions. This study shows a method of capturing uncertainty in geological heterogeneity when data are sparse or absent. The promising initial modelling results of CO 2 injection into the Surat Basin suggests that it presents a real option for carbon storage at a climate mitigation scale. Further investigation should focus on assessing other major risks associated with carbon storage such as fault seals, reactive fluid transport and the impact of legacy wells. This article is part of the Energy Geoscience Series available at https://www.lyellcollection.org/cc/energy-geoscience-series

A multi-objective and multi-scenario optimization model for operation control of CO2-flooding pipeline network system

Abstract Countries with high carbon emissions are actively exploring carbon capture, utilization and storage (CCUS) system. CCUS-based CO2 enhanced oil recovery (CO2-EOR) technology is favored for sustainable oilfield development and its contribution to mitigating global warming. In this paper, under the crafts constraints of injection stations and CO2-flooding wells, as well as the flow rate and pressure constraints along pipeline network, a multi-objective mixed integer nonlinear programming (MOMINLP) model is proposed for the optimal operation control of oilfield surface CO2-flooding pipeline network system. The minimum operating costs of pumps, the maximum CO2 injection volume and the minimum demand-injection volume deviation are set as objective functions. The uncertainty of demand CO2 injection volume caused by geological uncertainty is settled by scenario-based stochastic programming method. In addition, the piecewise linearization method and the augmented e-constraint method (AUGMECON) are introduced to deal with the nonlinear constraints and get the Pareto optimal solutions, respectively. Finally, the proposed model is successfully applied to a large-scale looped and branched CO2-flooding pipeline network system in Sinkiang, China with three cases for comparison to verify its applicability and superiority.