Regional studies are known to show major compartmentalization in an oil field, while observations during development and production often highlight local structural connectivity issues that require fault characterization at field-scale to mitigate uncertainty in reserve or stakes. The Akpo field, located in the deep offshore Niger Delta, exemplifies a maturing field where these structural connectivity issues are dominant and play significant roles in field development. Structural discrepancies in the crest and flanks of the anticline result in varying water contacts and overpressure differences, affecting connected volumes and sweep efficiency. Qualitative fault throw analysis, aided by 4-D monitoring results, show that same faults may be sealing and communicating at difference areas, across reservoir fairways in the deep offshore turbiditic channel complexes, delineated as architectural elements. Shale Gouge Ratio (SGR) helps in further constraining the sealing/leaking impact of fault gouge at a log-scale, such that adjacent well data can be used quantitatively to assess preferential flow paths across and within faults zones. This revealed an along-fault, up-fault and across-fault connectivity anisotropy. This work addresses how the fault characterization was used to assess the following: Reservoir compartmentalization, leading to panel separated as fault blocks.Communication across fault, shown by throw map and SGR.The varying water contacts, which tend to result from upwelling of fluid within panel.Sweep across panels, from injectors to producers.The well in real-time operational situation, where well trajectory traverses a fault. The study resulted in an improved infill well planning and placement, targeting unswept hydrocarbon, where well trajectories were determined by knowledge of fault compartmentalization, initial static connectivity shown by virgin pressures and present dynamic communication across injector-producer pairs. Post-mortem analysis of these infill wells was helpful in understanding the dynamic role of the crestal-collapse faults offsetting the reservoirs in the Akpo anticline, leading to optimization and increased productivity.
Natural gas is composed mostly of methane, the simplest hydrocarbon molecule, with only one carbon atom. But most gas at the wellhead contains other hydrocarbon molecules known as Natural Gas Liquids (NGL). Heavier gaseous hydrocarbons such as propane (C3H8), normal butane (n-C4H10), isobutane (i- C4H10) and pentanes, may also be processed in gas plants and exported as Liquified Natural Gas (LNG). During operational services in gas plant from inlet to outlet piping, gas leaks tend to occur undetected at some points in the facility. Apart from loss of gas resources, leaks and venting at natural gas processing plants release other pollutants besides methane (e.g., benzene, hexane, hydrogen sulfide) that can threaten air quality and public health. Hence, the need for early detection of gas leaks by using appropriate Machine Learning (ML) models. Insight from existing general flow equations was used to develop a new modelling approach for Machine Learning, in a test case: Gas Plant JK – 52. Input gas pressure data is calibrated and evaluated for consistency in real-time. The data is then corrected for lag-time and used to compute tolerance. Indicated time of alarm is checked against events such as residual gas, supply, pumping, etc. Where alarm is eventless, leak is suspected and eventually confirmed, suggesting that action should be taken to mitigate against the leakage. Following the input of a split training dataset, different types of regressions were used for the machine learning before automating the system for real-time evaluation and detection. Linear regression provided a 39% test accuracy, which was considered too low. This led to the use of random forest regression, which provided a 95% test accuracy and was considered excellent. It is hoped that with continuing data acquisition in gas plants employing this algorithm, further modelling will become more predictive as machine learns from experience.
Many fluid leak detection mechanisms rely on observation of volume changes and physical evidence of leak, which may take hours, days and sometimes weeks or months to be seen. This is a concern in gas plants where the proximity of the leakage may constitute environmental pollution as well as health hazards for personnel in the vicinity. Economic losses have also resulted from delays in mitigating a gas leak problem due to late detection. This study applies a machine learning technique to develop an algorithm that can detect gas leak in real-time, where the only possible delay is the lag-time between the inlet gauges at the upstream valve and the outlet gauge at the downstream valve. In this case study of JK-52 gas processing plant, the difference pressure gauge readings were calibrated against the volume of the gas in the inlet section to quantify the leak volume. Because gaseous fluids do not present physical indication of volume, a pressure-based method was used for the detection, where drop in gauge pressure due to de-pressurisation indicate leakage in the absence of recorded gas supply or collection. Python coding language, using Jupyter and Pycharm Integrated Development Environments (IDEs), was used for the programming. The machine learning algorithm analyses the incoming streaming pressure versus time datasets from the gauges during the residual and ramp-up flow phases to set the acceptable pressure difference cut-off. A minimun difference in gauge reading may be normal within an acceptable error margin. The change in the consistency of reading within this acceptable window defines the tolerance. The system is set-up to blare an alarm when there is leakage, usually based on a cut-off or tolerance, to be detected by the machine-aided process.
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