The Saudi Arabian tourism sector is growing, and its economy has flourished over the last decades. This has resulted in numerous coastal developments close to large economic centers, while many more are proposed or planned. The coastal developments have influenced the behavior of the shoreline in the past. Here we undertake a national assessment on the state of the coast of Saudi Arabia based on recent data sets on historic and future shoreline positions. While at national scale the shoreline is found to be stable over the last three decades, the Red Sea coast shows a regional-mean retreat rate while the Gulf coast shows a regional-mean prograding behavior. Detailed analysis of the temporal evolution of shoreline position at selected locations show that human interventions may have accelerated shoreline retreat along adjacent shorelines, some of which are Marine Protected Areas. Furthermore, reef-fronted coastal sections have a mean accretive shoreline change rate, while the open coast shows a mean retreat rate. Future shoreline projections under RCP 4.5 and RCP 8.5 show that large parts of the shoreline may experience an accelerated retreat or a change in its regime from either stable or sprograding to retreating. Under the high emission RCP 8.5 scenario, the length of coastline projected to retreat more than doubles along the Red Sea coast, and approximately triples along the Gulf coast in 2100. At national scale, the Saudi Arabian coastline is projected to experience regional-mean retreats of ~30 m and of ~130 m by 2050 and 2100 under both RCPs considered in this study. These results indicate that effective adaptation strategies will be required to protect areas of ecological and economic value, and that climate resilience should be a key consideration in planned or proposed coastal interventions.@en

An ensemble-based history-matching framework is proposed to enhance the characterization of petroleum reservoirs through the assimilation of crosswell electromagnetic (EM) data. As an advanced technology in reservoir surveillance, crosswell EM tomography can be used to estimate a cross-sectional conductivity map and associated saturation profile at an interwell scale by exploiting the sharp contrast in conductivity between hydrocarbons and saline water. Incorporating this information into reservoir simulation in combination with other available observations is expected to enhance the forecasting capability of reservoir models and to lead to better quantification of uncertainty. The proposed approach applies ensemble-based data-assimilation methods to build a robust and flexible framework in which various sources of available measurements can be integrated. A comparative study evaluates two different implementations of the assimilation of crosswell EM data. The first approach integrates the crosswell EM field components in their original form, which entails forward simulation of the observed EM responses from the simulated reservoir state. In the second approach, formation conductivity is derived from the EM data through inversion and is subsequently assimilated into the reservoir model. An image-oriented distance parameterization of the fluid front assimilates the conductivity field in an efficient and robust manner and overcomes issues with data size, errors, and their correlation. Numerical experiments for different test cases with increasing complexity provide insight into the performance of the two proposed integration schemes. The results demonstrate the efficiency of the developed history-matching workflow and the added value of crosswell EM data in enhancing the reservoir characterization and reliability of dynamic reservoir forecasts.@en

Hydrocarbon reservoir simulation models produce large amounts of heterogeneous data, combining multiple variables of different dimensionality, such as two or three-dimensional geospatial estimates with abstract estimates simulated for the complete field or different wells. In addition these simulations are nowadays often run as so-called ensemble simulations, to capture uncertainty of the model, as well as boundary conditions as variation in the output. The (visual) analysis of such data is a challenging process, due to the size and complexity of the data. In this paper we present an integrated system for the visual analysis of ensemble reservoir simulation data. We provide tools to inspect forecasts for multiple variables of complete fields, as well as different wells. Finally, we present a case study highlighting the effectiveness of the presented system.@en

Hydrocarbon reservoir simulation models produce large amounts of heterogeneous data, combining multiple variables of different dimensionality, such as two or three-dimensional geospatial estimates with abstract estimates simulated for the complete field or different wells. In addition these simulations are nowadays often run as so-called ensemble simulations, to capture uncertainty of the model, as well as boundary conditions as variation in the output. The (visual) analysis of such data is a challenging process, due to the size and complexity of the data. In this paper we present an integrated system for the visual analysis of ensemble reservoir simulation data. We provide tools to inspect forecasts for multiple variables of complete fields, as well as different wells. Finally, we present a case study highlighting the effectiveness of the presented system.@en