Population-based algorithms for improved history matching and uncertainty quantification of Petroleum reservoirs

In modern field management practices, there are two important steps that shed light on a multimillion dollar investment. The first step is history matching where the simulation model is calibrated to reproduce the historical observations from the field. In this inverse problem, different geological and petrophysical properties may provide equally good history matches. Such diverse models are likely to show different production behaviors in future. This ties the history matching with the second step, uncertainty quantification of predictions. Multiple history matched models are essential for a realistic uncertainty estimate of the future field behavior. These two steps facilitate decision making and have a direct impact on technical and financial performance of oil and gas companies. Population-based optimization algorithms have been recently enjoyed growing popularity for solving engineering problems. Population-based systems work with a group of individuals that cooperate and communicate to accomplish a task that is normally beyond the capabilities of each individual. These individuals are deployed with the aim to solve the problem with maximum efficiency. This thesis introduces the application of two novel population-based algorithms for history matching and uncertainty quantification of petroleum reservoir models. Ant colony optimization and differential evolution algorithms are used to search the space of parameters to find multiple history matched models and, using a Bayesian framework, the posterior probability of the models are evaluated for prediction of reservoir performance. It is demonstrated that by bringing latest developments in computer science such as ant colony, differential evolution and multiobjective optimization, we can improve the history matching and uncertainty quantification frameworks. This thesis provides insights into performance of these algorithms in history matching and prediction and develops an understanding of their tuning parameters. The research also brings a comparative study of these methods with a benchmark technique called Neighbourhood Algorithms. This comparison reveals the superiority of the proposed methodologies in various areas such as computational efficiency and match quality

Knowledge-Based Systems. Overview and Selected Examples

The Advanced Computer Applications (ACA) project builds on IIASA's traditional strength in the methodological foundations of operations research and applied systems analysis, and its rich experience in numerous application areas including the environment, technology and risk. The ACA group draws on this infrastructure and combines it with elements of AI and advanced information and computer technology to create expert systems that have practical applications. By emphasizing a directly understandable problem representation, based on symbolic simulation and dynamic color graphics, and the user interface as a key element of interactive decision support systems, models of complex processes are made understandable and available to non-technical users. Several completely externally-funded research and development projects in the field of model-based decision support and applied Artificial Intelligence (AI) are currently under way, e.g., "Expert Systems for Integrated Development: A Case Study of Shanxi Province, The People's Republic of China." This paper gives an overview of some of the expert systems that have been considered, compared or assessed during the course of our research, and a brief introduction to some of our related in-house research topics

Tracing back the source of contamination

From the time a contaminant is detected in an observation well, the question of where and when the contaminant was introduced in the aquifer needs an answer. Many techniques have been proposed to answer this question, but virtually all of them assume that the aquifer and its dynamics are perfectly known. This work discusses a new approach for the simultaneous identification of the contaminant source location and the spatial variability of hydraulic conductivity in an aquifer which has been validated on synthetic and laboratory experiments and which is in the process of being validated on a real aquifer

Resource Provision of the Sustainable Development under Global Shocks

This reprint focuses on interdisciplinary research that reveals the problems of resource provision of the economy, both from the perspective of local projects and from the point of view of the creation of global infrastructure that contributes to the achievement of Sustainable Development Goals. Considerable attention is paid to the development of the Arctic territories as one of the most promising sources of mineral and fuel resources as of 2021. This reprint also includes selected papers from European Raw Materials Conferences 2020–2021, held despite the global COVID-19 pandemic, and will be published with the financial support of the International competence Centre for mining-engineering education under the auspices of UNESCO: - Russian–UK Raw Materials Dialogue (21–23 October 2020); - Russian–German Raw Materials Conference (30 November–1 December 2020)

Paleokarst reservoir modelling - A concept-driven approach

A significant proportion of the world's hydrocarbon production comes from paleokarst reservoirs. Although these reservoirs boast some of the most productive wells in oil history, the recovery factor is relatively low (RFmean: 32%) compared to other carbonate reservoirs (RFmean: 37 - 51%). The low recovery could relate to current reservoir modelling approaches potentially yielding inaccurate resource estimates or early water-breakthrough. Conventional industry-standard reservoir modelling software suites do not have dedicated workflows or add-ins for handling the complex morphologies commonly associated with paleokarst. Current modelling approaches are often datadriven (conditioned on available seismic and well data) and employ adapted or modified versions of stochastic reservoir modelling workflows used for siliciclastic and carbonate reservoirs. However, many paleokarst features are below seismic resolution, and the representativity of individual well data is often challenging to assess. Consequently, data-driven models often fail to render the connectivity, geometry, and volume of karst features. Karst is the predecessor to paleokarst, and therefore a genetic approach employing existent information from recent karst systems may be a good starting point for generating analogues to paleokarst reservoirs. A concept-driven approach, in combination with current data-driven modelling approaches, may enable model rendering that more closely echoes actual paleokarst reservoir architectures. However, only a few conceptual modelling methods are publicly available and described in the literature. The drawbacks with the available methods are that they under-/overestimate the cave volumes, fail to provide realistic cave morphologies, and forecast clastic sediment infill, and do not differentiate between preserved and collapsed caverns. Consequently, post-collapse reservoir morphologies, volumes and facies distributions may be rendered inaccurately. This thesis aims to address the shortcomings of currently available conceptual methods and present a novel concept-driven workflow for paleokarst reservoir modelling. A novel methodology for geocellular rendering of karst systems is presented in this thesis. The method utilizes modern cave-survey data to generate dense, equally spaced point-clouds (infilling the cave periphery). These point clouds can be used to discretize the karst systems in a geocellular framework by geometrical modelling. The volumetric and geometric rendering of the method is compared with two pre-established methods and benchmarked against the cave survey. The results show that the new method offers improved volumetric and geometric geocellular rendering compared to the preestablished methods and are comparable to that of the cave survey. A pilot study using a well-known and pre-established geophysical method, electrical resistivity tomography (ERT), was carried out in the Maaras cave system in northern Greece to evaluate the large-scale volumetric significance and spatial distribution of clastic sediments infilling karst cavities. ERT proved to be a practical and useful method for differentiating mesoscale (>2.5 m2) stratigraphic heterogeneity. Resistivity contrasts allowed the identification of sedimentary thickness variations, interbedded breccias, and cave floor. Results showed that the siliciclastic sediment thickness varied from 25 m to >45 m, occupying a minimum of 69-95 % of the available accommodation space. Finally, a novel interactive tool for evaluating cavern stability and forward model collapse and infill processes was developed. The tool employs conventional cave survey data, field measurements and geomechanical data of the host rock to simulate potential post-collapse morphologies and generate spatial output data suitable for geocellular modelling. Collapse propagation, and eventually the volume affected by the collapse, is controlled by user-defined paleokarst facies proportions and associated average porosities following a “mass-balance-principle” (i.e., porosity is final and only redistributed over a larger volume). Three different collapse scenarios were modelled using the Agios Georgios cave system in northern Greece as an analogue. The results show that it is feasible to use cave surveys to simulate collapse and infill processes and estimate the final paleokarst reservoir architecture. The morphology, volume and relative facies-proportions rendered in the reservoir models are comparable to those calculated in the forward collapse modelling tool, indicating that the geocellular model echoes the simulation. The results also show that the vertical continuity and target volume of a reservoir increases significantly with increasing bedding dip. This suggests that improved forecasting of the final reservoir architecture may optimise well positioning, production planning and eventually improve recovery prediction.Doktorgradsavhandlin

Products and Services

Today’s global economy offers more opportunities, but is also more complex and competitive than ever before. This fact leads to a wide range of research activity in different fields of interest, especially in the so-called high-tech sectors. This book is a result of widespread research and development activity from many researchers worldwide, covering the aspects of development activities in general, as well as various aspects of the practical application of knowledge

Advances in Intelligent Robotics and Collaborative Automation

This book provides an overview of a series of advanced research lines in robotics as well as of design and development methodologies for intelligent robots and their intelligent components. It represents a selection of extended versions of the best papers presented at the Seventh IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications IDAACS 2013 that were related to these topics. Its contents integrate state of the art computational intelligence based techniques for automatic robot control to novel distributed sensing and data integration methodologies that can be applied to intelligent robotics and automation systems. The objective of the text was to provide an overview of some of the problems in the field of robotic systems and intelligent automation and the approaches and techniques that relevant research groups within this area are employing to try to solve them.The contributions of the different authors have been grouped into four main sections:• Robots• Control and Intelligence• Sensing• Collaborative automationThe chapters have been structured to provide an easy to follow introduction to the topics that are addressed, including the most relevant references, so that anyone interested in this field can get started in the area

Clean Hydrogen: Building Block of a New Geopolitical Landscape

Hydrogen and derived fuels (such as hydrogen-based ammonia and methanol) are currently enjoying renewed political and business momentum, based on the declining cost of renewable electricity and surging interest in deep decarbonization, as more and more countries rally behind net zero emission targets by mid-century. This chapter presents an overview of the geopolitical dimensions of hydrogen as a clean energy carrier. To that end, it first reviews the technical characteristics, different production methods and areas of application of hydrogen. Next, the chapter examines whether and how hydrogen and derived fuels could become globally traded energy commodities, and which countries are poised to become the hydrogen superpowers of the future. Finally, this chapter identifies six areas where hydrogen might shape geopolitics in the coming years: technology dominance, geo-economic competition, the future of petrostates, new interdependencies, carbon lock-in, and global governance

Advances in Intelligent Robotics and Collaborative Automation

This book provides an overview of a series of advanced research lines in robotics as well as of design and development methodologies for intelligent robots and their intelligent components. It represents a selection of extended versions of the best papers presented at the Seventh IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications IDAACS 2013 that were related to these topics. Its contents integrate state of the art computational intelligence based techniques for automatic robot control to novel distributed sensing and data integration methodologies that can be applied to intelligent robotics and automation systems. The objective of the text was to provide an overview of some of the problems in the field of robotic systems and intelligent automation and the approaches and techniques that relevant research groups within this area are employing to try to solve them.The contributions of the different authors have been grouped into four main sections:• Robots• Control and Intelligence• Sensing• Collaborative automationThe chapters have been structured to provide an easy to follow introduction to the topics that are addressed, including the most relevant references, so that anyone interested in this field can get started in the area