A review of closed-loop reservoir management

The closed-loop reservoir management technique enables a dynamic and real-time optimal production schedule under the existing reservoir conditions to be achieved by adjusting the injection and production strategies. This is one of the most effective ways to exploit limited oil reserves more economically and efficiently. There are two steps in closed-loop reservoir management： automatic history matching and reservoir production opti- mization. Both of the steps are large-scale complicated optimization problems. This paper gives a general review of the two basic techniques in closed-loop reservoir man- agement; summarizes the applications of gradient-based algorithms, gradient-free algorithms, and artificial intelligence algorithms; analyzes the characteristics and application conditions of these optimization methods; and finally discusses the emphases and directions of future research on both automatic history matching and reservoir production optimization.

Subsurface analytics: Contribution of artificial intelligence and machine learning to reservoir engineering, reservoir modeling, and reservoir management

Traditional Numerical Reservoir Simulation has been contributing to the oil and gas industry for decades.The current state of this technology is the result of decades of research and development by a large number of engineers and scientists.Starting in the late 1960s and early 1970s,advances in computer hardware along with development and adaptation of clever algorithms resulted in a paradigm shift in reservoir studies moving them from simplified analogs and analytical solution methods to more mathematically robust computational and numerical solution models.

Prediction of immiscible gas flooding performance:a modified capacitance-resistance model and sensitivity analysis

Reservoir performance prediction is one of the main steps during a field development plan.Due to the complexity and time-consuming aspects of numerical simulators,it is helpful to develop analytical tools for a rapid primary analysis.The capacitance-resistance model(CRM)is a simple technique for reservoir management and optimization.This method is an advanced time-dependent material balance equation which is combined with a productivity equation.CRM uses production/injection data and bottom-hole pressure as inputs to build a reliable model,which is then combined with the oil-cut model and converted to a predictive tool.CRM has been studied thoroughly for water flooding projects.In this study,a modified model for gas flooding systems based on gas density and average reservoir pressure is developed.A detailed procedure is described in a synthetic reservoir model using a genetic algorithm.Then,a streamline simulation is implemented for validation of the results.The results show that the proposed model is able to calculate interwell connectivity parameters and oil production rates.Moreover,a sensitivity analysis is carried out to investigate effects of drawdown pressure and gas PVT properties on the new model.Finally,acceptable ranges of input data and limitations of the model are comprehensively discussed.

Predictive data analytics application for enhanced oil recovery in a mature field in the Middle East

Top-Down Modeling(TDM) was developed through four main steps of data gathering and preparation, model build-up, model training and validation, and model prediction, based on more than 8 years of development and production/injection data and well tests and log data from more than 37 wells in a carbonate reservoir of onshore Middle-East. The model was used for production prediction and sensitivity analysis. The TDM involves 5 inter-connected data-driven models, and the output of one model is input for the next model. The developed TDM history matched the blind dataset with a high accuracy, it was validated spatially and applied on a temporal blind test, the results show that the developed TDM is capable of generalization when applied to new dataset and can accurately predict reservoir performance for 3 months in future. Production forecasting by the validated history matched TDM model suggest that the water production increases while oil production decreases under the given operation condition. The injection analysis of the history matched model is also examined by varying injection amounts and injection period for water and gas(WAG) process. Results reveal that higher injection volume does not necessarily translate to higher oil production in this field. Moreover, we show that a WAG process with 3 months period would result in higher oil production and lower water production and gas production than a 6 months process. The developed TDM provides a fast and robust alternative to WAG parameters, and optimizes infill well location.

Effects of artificially induced complete mixing on dissolved organic matter in a stratified source water reservoir

Naturally complete mixing promotes the spontaneous redistribution of dissolved oxygen(DO),representing an ideal state for maintaining good water quality,and conducive to the biomineralization of organic matter.Water lifting aerators(WLAs)can extend the periods of complete mixing and increase the initial mixing temperature.To evaluate the influence of artificial-induced continuously mixing on dissolved organic matter(DOM)removal performance,the variations of DOM concentrations,optical characteristic,environmental factors were studied after approaching the total mixing status via WLAs operation.During this process,the dissolved organic carbon reduced by 39.18%,whereas the permanganate index decreased by 20.47%.The optical properties indicate that the DOM became more endogenous and its molecular weight decreased.Based on the results of the Biolog Eco Plates,the microorganisms were maintained at a relatively high metabolic activity in the early stage of induced mixing when the mixing temperature was relatively high,whereas DOM declined at a high rate.With the continuous decrease in the water temperature,both the metabolic capacity and the diversity of aerobic microorganisms significantly decreased,and the rate of organic matter mineralization slowed down.The results of this study demonstrate that the artificial induced mixing largely enhanced the removal DOM performance by providing a long period of aerobic conditions and higher initial temperature.

Optimal Feedback Control of Oil Reservoir Waterflooding Processes

Waterfiooding is a process where water is injected into an oil reservoir to supplement its natural pressure for increment in productivity. The reservoir properties are highly heterogeneous, its states change as production progresses which require varying injection and production settings for economic recovery. As water is injected into the reservoir, more oil is expected to be produced. There is also likelihood that water is produced in association with the oil. The worst case is when the injected water meanders through the reservoir, it bypasses pools of oil and gets produced, Therefore, any effort geared toward finding the optimal settings to maximize the value of this venture can never be over emphasized. Waterflooding can be formulated as an optimal control problem. However, traditional optimal control is an open-loop solution, hence cannot cope with various uncertainties inevitably existing in any practical systems. Reservoir models are highly uncertain. Its properties are known with some degrees of certainty near the well-bore region only. In this work, a novel data-driven approach for control variable （CV） selection was proposed and applied to reservoir waterflooding process for a feedback strategy resulting in optimal or near optimal operation. The results indicated that the feedback control method was close to optimal in the absence of uncertainty. The loss recorded in the value of performance index, net present value （NPV） was only 0.26%. Furthermore, the new strategy performs better than the open-loop optimal control solution when system/model mismatch was considered. The performance depends on the scale of the uncertainty introduced. A gain in NPV as high as 30.04% was obtained.

Development of new correlations for the oil formation volume factor in oil reservoirs using artificial intelligent white box technique

Oil formation volume factor(OFVF)is considered one of the main parameters required to characterize the crude oil.OFVF is needed in reservoir simulation and prediction of the oil reservoir performance.Existing correlations apply for specific oils and cannot be extended to other oil types.In addition,big errors were obtained when we applied existing correlations to predict the OFVF.There is a massive need to have a global OFVF correlation that can be used for different oils with less error.The objective of this paper is to develop a new empirical correlation for oil formation volume factor(OFVF)prediction using artificial intelligent techniques(AI)such as;artificial neural network(ANN),adaptive neuro-fuzzy inference system(ANFIS),and support vector machine(SVM).For the first time we changed the ANN model to a white box by extracting the weights and the biases from AI models and form a new empirical equation for OFVF prediction.In this paper we present a new empirical correlation extracted from ANN based on 760 experimental data points for different oils with different compositions.The results obtained showed that the ANN model yielded the highest correlation coefficient(0.997)and lowest average absolute error(less than 1%)for OFVF prediction as a function of the specific gravity of gas,the dissolved gas to oil ratio,the oil specific gravity,and the temperature of the reservoir compared with ANFIS and SVM.The developed empirical equation from the ANN model outperformed the previous empirical correlations and AI models for OFVF prediction.It can be used to predict the OFVF with a high accuracy.

Distinct and combined impacts of climate and land use scenarios on water availability and sediment loads for a water supply reservoir in northern Morocco

The objective of this study was to examine the impacts of climate and land use changes on wateravailability and sediment loads for a water supply reservoir in northern Morocco using data-intensive simulation models in a data-scarce region. Impacts were assessed by comparing the simulated water and sediment entering the reservoir between the future period 2031-2050 and the 1983-2010 reference period. Three scenarios of land use change and two scenarios of climate change were developed in the Tleta watershed. Simulations under current and future conditions were performed using the Soil and Water Assessment Tool (SWAT) model. The simulations showed that climate change will lead to a sig-nificant decrease in the annual water supply to the reservoir (-16.9% and -27.5%) and in the annual volume of sediment entering the reservoir (-7.4% and -12.6%), depending on the climate change sce-narios tested. The three scenarios of land use change will lead to a moderate change in annual water inflow into the reservoir (between-6.7% and +6.2%), while causing a significant decrease in sediment entering the reservoir (-37%to-24%). The combined impacts of climate and land use changes will cause a reduction in annual water availability (-9.9%to-33.3%) and sediment supplies (-28.7%to-45.8%). As a result, the lifetime of the reservoir will be extended, but at the same time, the risk of water shortages will increase, especially from July to March. Therefore, alternative water resources must be considered.

Spatial and temporal assessment of the initial pattern of phytoplankton population in a newly built coastal reservoir

For decades, the main threat to the water security of a metropolis, such as the city of Shanghai, has been the rapidly growing demand for water and at the same time, the decrease in water quality, including eutrophica- tion. Therefore Shanghai shifted the preferred freshwater source to the Yangtze Estuary and constructed the Qingcaosha Reservoir, which is subject to less eutrophic water from the Yangtze River. To assess the population of phytoplankton for the first time in the newly built reservoir, this study improved an integrated method to assess the phytoplankton pattern in large-water-area reservoirs and lakes, using partial triadic analysis and Geographic Information Systems. Monthly sampling and monitoring from 10 stations in the reservoir from July 2010 to December 2011 were conducted. The study examined the common pattern of the phytoplankton population structure and determined the differences in the specific composition of the phytoplankton community during the transition period of the reservoir. The results suggest that in all but three sampling stations in the upper parts of Qingcaosha Reservoir, there was a strong common compromise in 2011. The two most important periods occurred from late summer to autumn and from winter to early spring. The former was characterized by the dominance of cyanobac- teria, whereas the latter was characterized by the dominance of both chlorophyta and diatoms. Cyanobac- teria （Microcystis spp. as the main genus） were the monopolistic dominant species in the summer after reservoir operation. The statistical analysis also indicated the necessity for regular monitoring to focus on the stations in the lower parts of the reservoir and on several limited species.

Risk Identification and Mitigation Strategies for Deepwater Oilfields Development

Deepwater oilfields will become main sources of the world's oil and gas production.It is characterized with high technology,huge investment,long duration,high risk and high profit.It is a huge system project,including exploration and appraising,field development plan(FDP)design,implementation,reservoir management and optimization.Actually,limited data,international environment and oil price will cause much uncertainty for FDP design and production management.Any unreasonable decision will cause huge loss.Thus,risk foreseeing and mitigation strategies become more important.This paper takes AKPO and EGINA as examples to analyze the main uncertainties,proposes mitigation strategies,and provides valuable experiences for the other deepwater oilfields development.