In most cases, retrograde gas reservoir in N Field which is located at the north of Malay Basin achieved 40-70% of recovery factor (RF) compared to dry gas reservoir, 80-90% of RF. Reservoir K, a lean retrograde gas reservoir of the N Field drained by Well 5 experience reduction in recovery (about 15% of RF) that is caused by a significant productivity loss, because of condensate banking. Condensate banking phenomenon (observed as skin) around the perforation zone restricted the flow of gas after the flowing bottom hole pressure falls below the dew point pressure. Therefore, the reduction in gas Inflow Performance Relationship (IPR) limits the Estimated Ultimate Recovery (EUR) of Reservoir K. It is the focus in this project to analyse the effect of miscible propane stimulated injection on the IPR, well deliverability and hence reservoir recovery. The retrograde gas reservoir model is integrated between E300, IPM PROSPER (well model) and IPM MBAL (reservoir model) software in reservoir performance prediction and forecasting study. Results show that there is 4% increment in gas recovery and 6% increment in condensate recovery after injection of propane to Reservoir K. There are about 4.2 million USD increment in revenue upon propane injection development. As conclusion, it is found that propane injection could minimize the condensate saturation that improves reservoir IPR and hydrocarbon recovery for both gas and condensate.
In this paper, enumeration method was developed to simulate gas condensate reservoir behavior based on the different condensate banking zones. Sharp increment of pressure drawdown near wellbore and description of condensate bank away the wellbore are still part of challenges in simulating gas condensate reservoir. This method allows prediction on how pressure behavior near and away wellbore and how the condensate banking will evolve in future due to the simultaneous flow of gas and condensate in the reservoir. A single well model has been developed to investigate the effect of condensate bank on pressure behavior in gas condensate reservoir. Upon production, gas condensate reservoir creates different zones based on different phase mobility, resulting from condensate banking. Enumeration initialization approach is to divide the simulation explicitly for the multiple stages. During the stops, condensate bank parameters have been modified at each zone to describe drastic change of well performance due to condensate banking. A representative matured gas condensate reservoir from Malay Basin has been selected for the purpose. The effect of the mobility changes to pressure behavior is then investigated. Comparison of pressure behavior between enumeration ways with conventional approach have been presented. It was found that enumeration method shows better prediction in investigating the pressure behavior and condensate saturation near and away wellbore during stabilized production. It is due to its ability to predict mobility changes at every zones. The change of relative permeability to variations in fluid saturation, velocity, and interfacial tension cause increment of pressure drawdown. In analytical analysis, the increment of pressure drawdown mostly demarcated as positive skin to a factor of 30 – 90. With enumeration method in place, pressure behavior can be quantified without defining the positive skin, thus improve the condensate bank characterization. This paper improves accuracy in defining complex physics of condensate banking phenomena, also providing key tool in quantifying the condensate bank. Enumeration approach screens the potential condensate banking in later well life and appropriate mitigation technique can be selected – optimize drawdown or change wettability as the positive skin term in simulation has been replaced.
Southeast Asia is increasingly gaining attention as a promising geological site for permanent CO2 sequestration in deep saline aquifers. During CO2 injection into saline reservoirs, the reaction between injected CO2, the resident formation brine, and the reservoir rock could cause injectivity change due to salt precipitation, mineral dissolution, and fine particles migration. The underlying mechanisms have been extensively studied, both experimentally and numerically and the governing parameters have been identified and studied. However, the current models that have been widely adopted to investigate reactive transport and its impact on CO2 injectivity have fundamental limitations when applied to solve small, high dimensional, and non-linear data. The objective of this study is to develop efficient and robust predictive models using support vector regression (SVR) integrated with hyperparameter tuning optimization algorithms, including genetic algorithm (GA). To develop the model, 44 datasets are used to predict the CO2 injectivity change with its influencing variables such as brine salinity, injection flow rate, particle size, and particle concentration. The performance for each model is analyzed and compared with previous models by determination of coefficient (R2), adjusted determination of coefficient (R¯2), average absolute percentage error (AAPE), root mean square error (RMSE) and mean absolute error (MAE). The model with the highest R2 is selected as the predictive model for CO2 injectivity impairment during CO2 sequestration in a saline aquifer. The results revealed that both SVR and GA-SVR are able to capture the precise correlation between measured and predicted data. However, the GA-SVR model slightly outperformed the SVR model by a higher R2 value of 0.9923 compared to SVR with R2 value of 0.9918. Based on SHAP value analysis, brine salinity had the highest impact on CO2 injectivity change, followed by injection flow rate, particle concentration, and jamming ratio. It was also found that hybridization of genetic algorithm with support vector regression does improve the model performance contrary to single algorithm and contributes to the determination of the most impactful factors that induce CO2 injectivity change. The proposed model can be upscaled and integrated into field-scale models to improve the optimization of CO2 injectivity in deep saline reservoirs.
Oil and gas industry have evolved towards digitalization and data are fully utilized for decision making, cost optimization, improve in efficiency, and increase productivity. Upstream sector in oil & gas produce a huge number of operation and production data in a real-time platform. It is tedious process that somehow impractical and inefficient to quality check and analyze all available data manually (Subrahmanya et al., 2014). By using machine learning algorithm, this can be improved to automate data quality check at scale. On top of that, imputation can also be implemented to substitute on missing data and future forecast in real-time. In a case of this study, a huge data was collected from more than 30,000 tags/sensors in real-time. The real-time data were collected up to seconds and quality check need to be done up to each data collected. Firstly, each equipment tags/sensors had been checked and arranged with P&ID drawing. Then, API was developed with the real-time platform. In this project, percentile of machine learning was applied and developed to quality checked the operation and production time-series data at scale. Lastly, the process was customized to other offshore platforms in the field. In addition to automated data quality checking, machine learning algorithms were also used to calculate missing information based on the underlying relationship between data points. These approaches would reduce time needed to maintain quality and reliable data for further analysis and usage. As a result, percentile in machine learning successfully automate the process of data quality check for more productivity and efficiency. The percentile was applied to understand, validate, and monitor data at scale. Anomalies were detected in real time that allows operators to analyze further on any possibility in faulty, damage, or loss. All the outliers, missing or wrong data were also recorded and visualized in a dashboard. The model also provides additional statistic to define stale and bad data on top of automated define parameters. These features have improved efficiency of data acquisition and preparation. As conclusion, the model assists operator in monitoring daily operation and production data efficiently. Data quality and reliability is the key factor in asset management to ensure operator trust on produced data. The quality checked data could be utilized for further analysis, troubleshooting, and decision making.
Bottom hole pressures are valuable source of information for reservoir surveillance and management and are the heart of reservoir engineering. Real – time pressure measurements record pressure data at 5 second interval resulting in enormous accumulation of data. The size and volume of the accumulated data limit the capability of existing analysis software to load and interpret data. This paper presents an improved methodology for data quality checking and data optimization in determining reservoir pressure depletion via Autoregressive Integrated Moving Average (ARIMA) and Decision Tree Model. Dataset was gathered from a representative reservoir from Malay Basin. The ARIMA algorithm presented was designed for quick and efficient data quality checking. The Decision Tree Model in other hand was utilized to select maximum buildup pressure for reservoir depletion point via well status parameters. The maximum pressures were selected from buildup up data when the decision tree conditions were met. Versus classical methods, the algorithm has obtained around 90% similarity. The resulting data were then can fully optimized for reserve reporting and forecasting study i.e. analysis and numerical simulation. The paper also reports on the advantages in the application of ARIMA – Decision Tree Algorithm in pressure surveillance revealing few key advantages namely minimize the need of well intervention and optimized workflow for reservoir engineer to view, utilize, and detect reservoir depletion data. ARIMA – Decision Tree Algorithm is targeted to be installed and integrated in field historian for better overall data analysis and visualization. Results produced from the ARIMA – Decision Tree Algorithm which consist of reservoir pressure depletion data will then improve more advance analysis such as simulation and forecasting in terms of overall speed and accuracy. As a conclusion, this paper presents the importance and application of incorporating Big Data Analytics Algorithm in reservoir management and reporting. Future work, deliverability calculations can be incorporated in the model to identify and rectify any abnormal reservoir behavior.
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