Inwell measurement of rates and ratios do not allow operators to proactively control flow into the well. For intelligent fields there is a growing need for tools that enable engineers to ‘see’ further into the reservoir in order to adjust and/or change the flow control strategy as required. From a reservoir management perspective the most value can be achieved when implementing intelligent flow control systems coupled with surveillance tools. Hence, this study presents a way of defining a fit for purpose surveillance strategy that can be coupled with active flow control systems that facilitates the optimisation of the recovery efficiency. A dynamic simulator was used to analyse and determine a monitoring strategy that provides the most cost effective method of understanding various phenomena occurring within the reservoir, such as water/gas encroachment. The intention of the study was to determine if the data set currently obtained is sufficient enough to provide a deep understanding of the reservoir. The dynamic simulator modelled the temperature, saturation and pressure changes and the post-processer was used to derive corresponding changes in resistivity and acoustic properties. The change in temperature was important to model in order to study the parameter for its surveillance value as well as to capture thermal effects on reservoir fluid properties and geomechanical properties. The electro-magnetic and acoustic properties were calculated using a well-known formulae; resistivity was derived by inverting the Archie equation. Results from the study were used to specify what the surveillance tools should measure and what detection distance would allow operators to proactively adjust the flow control devices/valves in order to maximise the value of the development. Sensitivity and optimisation analysis was conducted to obtain the distance of investigation required by the surveillance. The study demonstrated that a simple 1-way coupling gives representative estimations of electro-magnetic and acoustic properties over field production time. This will be able to assist in determining which methods of surveillance can be used over the life of the field assisting in recommending a suitable data acquisition strategy to extract maximum value from the tools available; DAS/DTS/DSS/DPS/DxS, EM, seismic and other methods. Simple 1-way coupling is often enough to estimate the change with suitable accuracy to assess feasibility of various surveillance techniques. DAS could have an application in continuous cross-well tomography to follow the flood front; this subject is to be studied further.

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Carbon capture and storage (CCS) is recognized as an important technology in the decarbonisation of the energy system and saline aquifers are potential geological storage candidates. A major integrated feasibility study was conducted to screen and rank carbonate saline aquifer candidates for subsurface CO2 storage, onshore Abu Dhabi. The objectives were to obtain a range of potential CO2 storage capacities and annual injection rates and establish CO2 technical feasibility by integrating subsurface, well performance, cap rock integrity and economic analysis. A candidate screening matrix was developed taking into account onshore Abu Dhabi saline aquifer geological characteristics. Saline aquifers "A" and "B" within the syncline area were among the highest ranked candidates. A large-scale 3D static model was developed, utilising seismic and well data. Extensive CO2 storage simulation runs were performed, covering sensitivities and capturing major storage process/mechanisms applicable to carbonate formation. Combining geomechanics, geoscience, well performance, integrity and dynamic modelling, a CO2 storage site design was completed with slanted/horizontal injectors drilled radially from a centralised well pad. Ranges of CO2 storage capacity and maximum injection rates were obtained, depending on number of injectors and accounting for water offtake in nearby areas. Additionally, CO2 plume migration within several tens of thousands of years was simulated to aid CO2 containment assurance. Separate studies were performed to locate potential CO2 storage surface sites and used as part of the input for CO2 pipeline and surface facilities high level design. CAPEX, OPEX and abandonment cost estimates were generated as input for economic analysis. A multi-disciplinary risk assessment was performed, identifying potential risk factors throughout the life cycle of CO2 storage. De-risking and mitigation measures were considered and a detailed measurement, monitoring and verification (MMV) plan was developed. This paper presents the first integrated study on saline aquifer CO2 storage technical feasibility in this syncline area. A novel integrated workflow is employed, from initial candidate screening through dynamic modelling, surface facilities and risk assessment to recommendations for additional data acquisition. Key aspects which improved on published major international CO2 sequestration assessments are highlighted. The results and conclusions offer valuable insights for other Operators considering or planning CO2 sequestration in saline aquifer projects.

The Greater Burgan field in South-East Kuwait is the world's largest sandstone oilfield and the second-largest conventional oilfield. The Wara reservoir, in the Greater Burgan field, is a prolific sandstone oil-producing formation. Peripheral water injection into the Wara reservoir is in progress for pressure maintenance and to improve oil recovery from the flank areas. Polymer injection has also been identified as a practical EOR method that can potentially increase oil production and recovery from the Wara reservoir. In view of that and, as a follow-up to a previous Long-Term Polymer Injectivity Test (LTPIT) (Murayri et al. 2022), a second LTPIT was carried out targeting a different area within the Wara reservoir. This paper describes elements of the polymer injection predictions approach, results obtained from a dynamic simulation sector model, before and after polymer injection, in pursuit of phased commercial polymer-flooding development using fit-for-purpose modularized water treatment and polymer mixing/injection facilities. Prior to the commencement of polymer injection, a representative 3x3 km sector was extracted from the full-field dynamic model. A fine grid numerical simulation model was then history matched and calibrated using production/injection history and Step Rate Test (SRT), Pressure Fall-Off (PFO), and Injection Logging Tool (ILT) and High Precision Temperature-Spectral Noise Logging (HPT-SNL) surveillance data. This model was set for predicting polymer injection rates to ensure injection under matrix conditions, at different polymer concentrations, to guide field implementation over a period of 3 months. Pre-LTPIT modeling results demonstrated that injection at commercial rates of >2,000 bpd is possible with polymer concentrations ranging from 1,500 to 1,800 ppm in accordance with the targeted in-situ polymer solution viscosity. During LTPIT field implementation, downhole pressure and temperature were monitored real-time in addition to wellhead pressure, injected polymer solution viscosity and injection rates to evaluate performance and update the sector model. Thereafter, reservoir simulation sensitivity runs were extensively investigated to design an optimal phased commercial development plan. This plan was developed by optimizing well requirements, injected polymer Pore Volume (PV) and concentration. A polymer PV of 0.8 and a concentration of 1,800 ppm were recommended accordingly in conjunction with 40 acre inverted 5-spot patterns. Economic evaluation was performed while considering water-flooding performance as a baseline. The incremental benefits associated with oil production gains and reduced water handling requirements were evaluated against the envisioned investment in additional wells and polymer injection. The optimal case showed an incremental oil recovery factor of 7% over a period of 10 years. This paper presents a case study wherein fit-for-purpose reservoir modelling is integrated with LTPIT surveillance/monitoring data to maximize the techno-economic benefits of phased commercial polymer-flooding in the Wara reservoir of the Greater Burgan field.

Unconventional reservoir development is a multidisciplinary challenge due to complicated physical system, including but not limited to complicated flow mechanism, multiple porosity system, heterogeneous subsurface rock and minerals, well interference, and fluid-rock interaction. With enough well data, physics-based models can be supplemented with data driven methods to describe a reservoir system and accurately predict well performance. This study uses a data driven approach to tackle the field development problem in the Eagle Ford Shale. A large amount of data spanning major oil and gas disciplines was collected and interrogated from around 300 wells in the area of interest. The data driven workflow consists of: Descriptive model to regress on existing wells with the selected well features and provide insight on feature importance, Predictive model to forecast well performance, and Subject matter expert driven prescriptive model to optimize future well design for well economics improvement. To evaluate initial well economics, 365 consecutive days of production oil per CAPEX dollar spent (bbl/$) was setup as the objective function. After a careful model selection, Random Forest (RF) shows the best accuracy with the given dataset, and Differential Evolution (DE) was used for optimization. Using recursive feature elimination (RFE), the final master dataset was reduced to 50 parameters to feed into the machine learning model. After hyperparameter tuning, reasonable regression accuracy was achieved by the Random Forest algorithm, where correlation coefficient (R2) for the training and test dataset was 0.83, and mean absolute error percentage (MAEP) was less than 20%. The model also reveals that the well performance is highly dependent on a good combination of variables spanning geology, drilling, completions, production and reservoir. Completion year has one of the highest feature importance, indicating the improvement of operation and design efficiency and the fluctuation of service cost. Moreover, lateral rate of penetration (ROP) was always amongst the top two important parameters most likely because it impacts the drilling cost significantly. With subject matter experts’ (SME) input, optimization using the regression model was performed in an iterative manner with the chosen parameters and using reasonable upper and lower bounds. Compared to the best existing wells in the vicinity, the optimized well design shows a potential improvement on bbl/$ by approximately 38%. This paper introduces an integrated data driven solution to optimize unconventional development strategy. Comparing to conventional analytical and numerical methods, machine learning model is able to handle large multidimensional dataset and provide actionable recommendations with a much faster turnaround. In the course of field development, the model accuracy can be dynamically improved by including more data collected from new wells.