Development of geological models of the sub-surface relies on the interpretation of largely remotely sensed data. We propose a program of knowledge exchange that shares existing information and trials new methods for determining the impact of human biases, anchoring and confidence, on the interpretation of data used to build geological models. From this knowledge exchange and creation we will create and promote optimal workflows for interpretation that minimize risk in the oil and gas industry from interpretational uncertainty. The geological exploration and production of hydrocarbons and the storage of CO2 in geological reservoirs requires a 3D picture to be built of the sub-surface. This picture is made up of remotely sensed information like seismic reflection data with poor resolution, and 1D point sources such as well bores which sample a relatively small amount of the sub-surface volume of interest. Work on improving interpretation of these datasets has mainly focused on technological improvements to refine the imaging and processing of the remotely sensed data to better illuminate the sub-surface architecture. But even with improved techniques interpretations of the data, and the subsequent models created are uncertain. This uncertainty equates to exploration and production risk. The risk results from the lack of constraint from the data to create a 'certain' predictive model, and is amplified by known biases that are applied during interpretation of limited datasets. This knowledge exchange proposal aims to: quantify the effect of known biases on interpretation of seismic reflection datasets and to build a workflow that minimizes biases in interpretation that industry can deploy. We will work with industry, and on industry datasets, to exchange knowledge of industry workflows and the effects of human bias between the academics and partner companies involved, as well as with MSc and PhD students. Building on this exchange we will create new knowledge through a series of experiments to investigate and quantify the influence of anchoring on interpretation. By building into the experiment release of additional data we will test how individual's deal with new information that either confirms, or is contrary, to their original interpretation; and for how long individuals remain anchored to an original prediction in the face of contradictory evidence. We will compare cohorts of individuals with staged access to different data against those with all the data at the outset. Throughout the process we will gauge an individual's perception of confidence in their interpretation through an expert elicitation process. Using this new knowledge we will quantify the impact of human biases on interpretational uncertainty and determine an optimal workflow for seismic interpretation. From our combined existing and co-generated knowledge we will create a series of products to promote this workflow, and the associated knowledge, as well as the NERC science on which they are based. These will include an online resource of digital video footage deployed through the existing Virtual Seismic Atlas, accessed by 8,000-10,000 users monthly, and a series of training packages for industry and early career scientists undertaking PhDs as part of the NERC Oil and Gas Centre for Doctoral Training.

Process planning and scheduling problems are becoming increasingly complex due to the expanding production and customer base around the globe. A decision maker is continuously faced with the challenge to optimise the production plans and reduce costs under uncertainty. The uncertainty can be attributed to factors including volatile customer demands, variations in the process performance, fluctuations in socio-economics around the locations of the production plants, etc. Another complicating issue is the time-scale at which the decisions have to be taken and implemented. Not being able to effectively take these issues into account can lead to increased costs, customer dissatisfaction, loss of competitive edge and eventually shutting down of the manufacturing bases. This project aims to develop planning and scheduling tools for optimal decision-making under uncertainty while taking into account the multiple time-scales. Each process planning and scheduling problem is unique and hence one modelling and model solution tool cannot address the peculiarities of each problem. A framework where uncertainties are classified into specific categories is the key to providing cutting-edge optimal solutions. So, a problem will have a number of uncertainties which will be classified based upon our proposed framework and then for each classification the appropriate solution methodology will be invoked. A hybrid uncertainty modelling and optimisation tool that exploits the synergies of the solution techniques for various classes of uncertainty will also be developed. The novel planning and scheduling tools developed in this project will be tested on real-life case studies from process industries from a wide variety of sectors including energy systems, agrochemicals, pharmaceuticals, consumer goods, oil & gas, and industrial gases. Optimal planning and scheduling solutions based upon personalised uncertainty will be obtained.

The year 2011 recorded the highest ever global consumption of energy, estimated at more than 12 billion tonnes of oil equivalent. Because of this, and despite increasingly widespread deployment of renewable energy generation, annual global emissions of greenhouse gases are continuing to rise, underpinned by increasing consumption of fossil fuels. Carbon capture and storage (CCS) is currently the only available technology that can significantly reduce CO2 emissions to the atmosphere from fossil fuel power stations and other industrial facilities such as oil refineries, steel works, cement factories and chemical plants. However, achieving meaningful emissions reduction requires wide deployment of large scale CCS and will involve long term storage of very large volumes of CO2 in the subsurface. Ultimately, if CCS were to be rolled out globally, volumes of injected carbon dioxide could become comparable, on an annual basis, to world hydrocarbon production. The most likely sites for CO2 storage are depleted oil and gas fields or saline aquifers. Understanding and monitoring geomechanical processes within different types of storage site is crucial for site selection, for achieving long term security of storage and for instilling wider confidence in the safety and effectiveness of CCS. In many cases depleted hydrocarbon fields have experienced strong pressure decrease during production which may have affected the integrity of the caprock seal; furthermore, CO2 injection into saline aquifers will displace large volumes of groundwater (brine). In all cases, as injection proceeds and reservoir pressures increase, maintaining the geomechanical stability of the storage reservoir will be of great importance. Understanding and managing these subsurface processes is key to minimising any risk that CO2 storage could result in unexpected effects such as induced earthquakes or damage to caprock seal integrity. Experience from existing large-scale CO2 injection sites shows that monitoring tools such as time-lapse 3D seismic, micro-seismic monitoring and satellite interferometry have the potential to make a significant contribution to our understanding of reservoir processes, including fine-scale flow of CO2, fluid pressure changes, induced seismic activity and ground displacements. The DiSECCS project will bring together monitoring datasets from the world's three industrial scale CO2 storage sites at Sleipner (offshore Norway), Snohvit (offshore Norway) and In Salah (Algeria) to develop and test advanced and innovative monitoring tools and methods for the measurement and characterisation of pressure increase, CO2 migration and fluid saturation changes and geomechanical response. A key element of the research will be to identify those storage reservoir types that will be suitable for large-scale CO2 storage without unwanted geomechanical effects, and to develop monitoring tools and strategies to ensure safe and effective storage site performance. In addition, our research will explore public attitudes to CO2 storage. We will consider what insights may be drawn from previous proposed CCS schemes involving onshore storage and other activities that have aroused similar concerns (such as earthquakes associated with shale gas fracking near to Blackpool) and how this experience can inform proposed large-scale offshore storage operations in the future. In the past, public opposition to some onshore storage proposals has led to project delays and cancellation, for example, in the Netherlands, Denmark and Germany, and research has identified storage as the stage in the CCS chain that has most potential for concern to members of the lay public. Developing an improved understanding of potential societal responses to CO2 storage and monitoring is crucial for establishing a sustainable and successful CCS strategy; this research will contribute to this through a combination of case study analysis and participatory research with lay citizens.