Numerical modeling of self-potential in heterogeneous reservoirs

Monitoring water movement toward production wells through downhole measurements of self-potential (SP) was a promising new technology. However, there were uncertainties about its applicability in heterogeneous, multilayered reservoirs. Using numerical modeling, we investigated the likely magnitude and behavior of SP during oil production supported by water injection in two different models of such reservoirs. We found that the magnitude of the SP signal that would be measured along a production well increased as water approached the well, exceeding an assumed noise level of 0.1 mV before water breakthrough. We also found that, in the reservoir models tested, the maximum value of SP at the well skewed toward the fastest waterfront before water breakthrough. The trend of SP increasing at the well with time, together with the shape of the SP profile, were the prime indicators used to investigate water movement. In the reservoir models tested, before water breakthrough the fastest approaching waterfront could be detected approximately 20 m away from the well. However, subsequent waterfronts approaching the well in other layers could not be detected before breakthrough. The effect of these later waterfronts on the SP profile at the well was only detectable at breakthrough. We attributed this to the fact that the SP generated in these layers is masked by the high SP created by the fastest waterfront. Our findings emphasized the importance of an enhanced understanding of reservoir geology and rock electrical properties for better prediction and interpretation of SP.

The Effect of Surfactant Polymer Concentration on the Distribution of Oil Drainage Area in Heterogeneous Reservoir

The use of chemical injection has been widely used in the oil field on a large scale. One of the enhanced oil recovery (EOR) methods to increase production from old oil fields is through polymer surfactant injection, which functions to reduce interfacial tension and water-oil mobility ratio. This study focuses on developing a simulation model for chemical injection of polymer surfactant reservoirs by hypothetically making heterogeneous reservoir models in each layer with dimensions of 10x10x4. It consists of one a vertical well which is producer well located at the top of the left corner and one an injection well which is located at the bottom of right corner. This study shows a comparison between surfactant injection, polymer injection and SP injection using the same surfactant and polymer concentration with a concentration of 1000 ppm with 0.3 PV. Oil recovery in polymer injection turned out to be quite high compared to other chemical injections. In polymer injection, the oil recovery was 4.17%. Meanwhile, surfactant injection and SP injection increased by 0.59% and 0.61, respectively.

Improved Simulation Efficiency for Optimising Inflow Control Device Completions Using Suitably Upscaled Models and Parallel Global Search Optimisation Techniques

Inflow Control Device (ICD) completions can improve well performance by adjusting the inflow profile along the well and reducing the influx of unwanted fluids. The ultimate aim of using ICD completions is to provide maximum oil recovery and/or Net Present Value (NPV) over the life of the field. Proactive ICD optimisation studies use complex reservoir models and high-dimensional nonlinear objective functions to find the optimum ICD configurations over the life of the field. These complex models are generated from fine scale detailed geological models to accurately capture fluid flow behaviour in the reservoir. Although these high-resolution geological models can provide better performance predictions, their simulation runtimes can be computationally expensive and time consuming for performing proactive ICD optimisation studies that often require thousands of simulation runs. We propose a new workflow where we use upscaled and locally refined models coupled with parallelised global search optimisation techniques to improve the simulation efficiency when performing ICD optimisation and decision-making studies. Our approach preserves the flow behaviour in the reservoir and maintains the interaction between the reservoir and the well in the near wellbore region. Moreover, when coupled with parallel optimisation techniques, the simulation time is significantly reduced. We present an in-house code that couples global search optimisation algorithms (Genetic Algorithm and Surrogate Algorithm) with a commercial reservoir simulator to drive the ICD configurations. We evaluate the NPV as the objective function to determine the optimum ICD configurations. We apply and benchmark our approach to two different reservoir models to compare and analyse its efficiency and the optimisation results. Our analysis shows that our proposed approach reduces the run time by more than 80% when using the upscaled models and the parallel optimisation techniques. These results were based on a standard dual-core parallel desktop configuration. Additional results also showed further reduction in run time is possible when employing more processors. Additionally, when testing different ICD completion strategies (ICDs in producers only, ICDs in injectors only, and ICDs in both producers and injectors), the NPV can be increased by 9.6% for the optimised ICD completions. The novelty of our work is rooted in the much-improved simulation efficiency and better performance predictions that supports ICD optimisation and decision-making studies during field development planning to maximize profit and minimize risk over the life of the field.

Probabilistic Economical Evaluation for Business Decisions Through Integrated Uncertainty Assessment and Reliable Ensemble-Based Production Forecasts

Economic evaluation of exploration and production projects ensures a positive return for asset operators and stakeholders and evaluates risk in field development decisions related to both reservoir model uncertainties and fluctuations in oil and gas prices. Traditionally, such evaluation is performed manually and deterministically using single or limited number of cases (limited number of reservoir models and few values of economic parameters). Such traditional approach does not integrate seismic-to-simulation reservoir model uncertainties, the reservoir model used is often unreliable due to inconsistent property modifications during the history matching process, full span of prediction uncertainty isn't properly propagated for economic evaluation and the whole process is not fully automated. This paper presents an integrated and automated forward modelling approach where static and dynamic models are connected to integrate the impact of uncertainties at the different modelling stages (seismic interpretation through geological modelling to dynamic simulation and further to economic evaluations). The approach is demonstrated using synthetic 3D model data mimicking a real North Sea field. It starts by building an integrated modelling workflow that can capture the various reservoir model uncertainties at different stages to automatically generate multiple probable model realisations. Proxy models are constructed and used to refine the history match in successive batches. For each prediction development scenario, prediction probabilities are estimated using posterior ensemble of geologically consistent runs that matches historical observed data. The ensemble of reservoir models is automatically evaluated against different possible economic scenarios. The approach presents a seamless and innovative workflow that benefits from new-generation hardware and software, enables faster simultaneous realisations, produces consistent and more reliable reservoir models. Probabilistic economic evaluation concept is implemented to calculate the statistical probabilities of economic indicators.

Real-Time 3D Ultra Deep Directional Electromagnetic LWD Inversions: An Innovative Approach for Geosteering and Geomapping Water Slumping Movement Around Sub-Seismic Fault, Onshore Abu Dhabi

An Ultra-Deep Directional Electromagnetic LWD Resistivity (UDDE) tool was deployed in a mature Lower Cretaceous carbonate reservoir to map injection water movement. These thick carbonate reservoirs experience injection water preferentially travelling laterally at the top of the reservoir. The water held above oil by negative capillary forces slumps quickly, leading to increasing water cut, eventually killing the natural lift horizontal producing well. Real time 3D and 1D inversions provided important accurate mapping of the non-uniform water fronts and reservoir boundaries, providing insights into reservoir architecture and water movement. The candidate well is located in an area of significant uncertainty regarding fluid distribution and structural elements like sub-seismic faults etc. Pre-well 1D inversion results indicated that the water slumping front away from wellbore can be mapped within a vertical radius of 60-100 ft TVD. However, 1D inversion is not accurate where steeply dipping or discontinuous formations exist due to the presence of faults and is expected to impact well placement, mapping water fronts / formation boundaries and long-term oil recovery. Therefore in the real time, full 3D and 1D inversions of the Ultra-Deep EM data were run to provide high quality reservoir imaging in this complex geometrical setting and deliver improved reservoir fluid distribution and structure mapping. The pre-well inversion modeling optimized the frequency and transmitter-receiver spacing of the UDDE tool. The bottom hole assembly (BHA) configuration also included conventional LWD tools such as Neutron-Density, propagation Resistivity and Gamma Ray. Multiple 3D inversion datasets were processed in real-time using different depths of inversion ranging from 50 ft up to 120 ft depth. The 3D inversion results during the real-time drilling operation detected the non-uniform waterfront boundaries and water slumping up to 80 ft TVD above the wellbore using a slimhole (4¾″) tool. An interpreted sub-seismic down-thrown fault was mapped which controlled the non-uniform slumping fluid distribution, causing the water front to approach closest to the wellbore in this location. This suggests that the fault zone is open and provides a degree of increased permeability around the plane of the fault. The real-time 3D inversion, 1D shallow and 1D deep inversion results showed comparable structural imaging despite being inverted independently of each other. These results permitted updates to the static / dynamic reservoir models and an optimization of the completion design, to delay the water influx and thereby sustain oil production for a longer period of time. Field wide implementation of the UDDE tool and its advanced technology with improved 1D and 3D inversion results will enhance the quality of realtime geosteering, mapping and updating of reservoir models which have challenging water slumping fronts and structural variations. This will enable improvment in well locations, their spacing and finally allowing the proactive design of smart completions for enhanced oil production and improved recovery factors.

An Innovative Approach for Simulating CO2 Injection in Reservoir Models

Decarbonization is playing a major role in the near-future strategies of all the major oil and gas companies and one of most promising activity is the Carbon Capture and Storage (CCS). CCS consists in capturing CO2 coming from an industrial process and storing it in subsurface. In this project, three depleted reservoirs have been identified to inject CO2. Despite being located very close to each other, the three reservoirs are not in communication and the same surface facilities would be used for injection. The objective is to develop a suitable workflow for reservoir simulation to evaluate different injection scenarios. For this project, two wet gas reservoir and a light oil reservoir have been considered. A unique fluid description is not practical given the peculiarities of these reservoirs, as well as the construction of a single reservoir model. Currently there are some limitations in commercial solution to handle reservoirs coupling with different fluid description. A workflow has been developed using a controller that manages modules for simulating the whole asset. Injection rate of each well is calculated based on well condition and injection strategy. This process is performed for all the timestep of forecast. This solution guarantees to simulate the CO2 injection in three reservoirs in parallel in a reasonable simulation time (less than 2 hours), demonstrating the capability of overcoming the limitation of a commercial reservoir simulator related to the coupling of fields with different fluid properties. Different scenarios have been simulated considering alternative amount of CO2 to be injected. The gas injection scenario is fully accommodated inside the three reservoirs for all simulated scenarios. Moreover, the injection strategy is based on homogeneous re-pressurization of the three reservoirs and minimization of a possible well unbalancing. To achieve this objective, optimal weights to each field can be assigned to allocate the injection rates. The output of this simulation acts as primary input for dedicated studies (Cap Rock integrity, Thermally Induced Fracturing, Flow Assurance…) with the main advantage of being fully integrated at regional scale. The workflow applied in this project go beyond the main limitations of a standard reservoir coupling model. In particular, 3D reservoir models with different fluid description based on different equation of states, cannot be coupled using the standard workflows of the reservoir simulators, and anyway the available solutions are not fast and easy to implement. This approach provides a robust and flexible evaluation of the CO2 injection scenario in multiple reservoirs.

Preliminary results of XRF and XRD analyzes for lithofacial characteristics of the Doirentsi Formation reservoir carbonates in the south-central part of the Moesian Platform

The main objective of the study is to determine the lithofacial characteristics of the Doirentsi Formation reservoir carbonates in the range of the south-central part of the Moesian platform by XRD and XRF analyzes. The obtained results demonstrate the chemical and phase composition and supplement the field description of the Middle Triassic limestones and dolomites. This will allow the creation of realistic 3-D reservoir models for gas condensate fields Pisarovo and Devetaki in the future.

Differences in the Upscaling Procedure for Compositional Reservoir Simulations of Immiscible and Miscible Gas Flooding

Compositional reservoir simulation is essential to represent the complex interactions associated with gas flooding processes. Generally, an improved description of such small-scale phenomena requires the use of very detailed reservoir models, which impact the computational cost. We provide a practical and general upscaling procedure to guide a robust selection of the upscaling approaches considering the nature and limitations of each reservoir model, exploring the differences between the upscaling of immiscible and miscible gas injection problems. We highlight the different challenges to achieve improved upscaled models for immiscible and miscible gas displacement conditions with a stepwise workflow. We first identify the need for a special permeability upscaling technique to improve the representation of the main reservoir heterogeneities and sub-grid features, smoothed during the upscaling process. Then, we verify if the use of pseudo-functions is necessary to correct the multiphase flow dynamic behavior. At this stage, different pseudoization approaches are recommended according to the miscibility conditions of the problem. This study evaluates highly heterogeneous reservoir models submitted to immiscible and miscible gas flooding. The fine models represent a small part of a reservoir with a highly refined set of grid-block cells, with 5 × 5 cm2 area. The upscaled coarse models present grid-block cells of 8 × 10 m2 area, which is compatible with a refined geological model in reservoir engineering studies. This process results in a challenging upscaling ratio of 32 000. We show a consistent procedure to achieve reliable results with the coarse-scale model under the different miscibility conditions. For immiscible displacement situations, accurate results can be obtained with the coarse models after a proper permeability upscaling procedure and the use of pseudo-relative permeability curves to improve the dynamic responses. Miscible displacements, however, requires a specific treatment of the fluid modeling process to overcome the limitations arising from the thermodynamic equilibrium assumption. For all the situations, the workflow can lead to a robust choice of techniques to satisfactorily improve the coarse-scale simulation results. Our approach works on two fronts. (1) We apply a dual-porosity/dual-permeability upscaling process, developed by Rios et al. (2020a), to enable the representation of sub-grid heterogeneities in the coarse-scale model, providing consistent improvements on the upscaling results. (2) We generate specific pseudo-functions according to the miscibility conditions of the gas flooding process. We developed a stepwise procedure to deal with the upscaling problems consistently and to enable a better understanding of the coarsening process.

Numerical Comparison Between ES-MDA and Gradient-Based Optimization for History Matching of Reduced Reservoir Models

The Ensemble Smoother with Multiple Data Assimilation (ES-MDA) method has been popular for petroleum reservoir history matching. However, the increasing inclusion of automatic differentiation in reservoir models opens the possibility to history-match models using gradient-based optimization. Here, we discuss, study, and compare ES-MDA and a gradient-based optimization for history-matching waterflooding models. We apply these two methods to history match reduced GPSNet-type models. To study the methods, we use an implementation of ES-MDA and a gradient-based optimization in the open-source MATLAB Reservoir Simulation Toolbox (MRST), and compare the methods in terms of history-matching quality and computational efficiency. We show complementary advantages of both ES-MDA and gradient-based optimization. ES-MDA is suitable when an exact gradient is not available and provides a satisfactory forecast of future production that often envelops the reference history data. On the other hand, gradient-based optimization is efficient if the exact gradient is available, as it then requires a low number of model evaluations. If the exact gradient is not available, using an approximate gradient or ES-MDA are good alternatives and give equivalent results in terms of computational cost and quality predictions.