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Shallow groundwater is the primary drinking water source for local communities surrounding Duri Field operations area. To ensure the local community has access to safe drinking water and to comply with Government of Indonesia (GOI) environmental safety regulations, PT. Chevron Pacific Indonesia (PT CPI) established a groundwater monitoring system to ensure that slurry waste injection into deep non-potable water aquifers does not contaminate nearby shallower drinking water aquifers. The prevention system also includes well integrity surveillance and monitoring as a leading indicator and water sampling and analysis as a lagging indicator. Duri Field is in the Rokan block, Riau Province of Indonesia. Oily sand and viscous fluids are byproducts from oil production and treatment processes. The Sand Management Facility (SMF) was developed to manage these solid and liquid wastes by injecting mixed waste slurry under high pressure into deep subsurface formations. The scope of this study included 12 injector wells with two groundwater monitoring wells surrounding each injection well. Stable isotope monitoring technology is used to ensure that deep waste injection slurry is not mixing with and contaminating shallow drinking water resources. Stable isotope (δ2H, δ18O) analysis was used to define the Local Meteoric Water Line (LMWL) of Rokan Watershed defined by equation δ2H = 7.6 δ18O + 10.5 (r2 = 0.921), which is applied as a reference point for isotope studies in SMF area. The stable isotope δ2H samples for groundwater in SMF ranged from −70.5 ‰ to −25.1 ‰ followed by formation and surface waters that respectively ranged from −64.8 ‰ to −48.9 ‰ and from −61.9 ‰ to −20.8 ‰, while δ18O samples ranged from −11.18 ‰ to −2.12 ‰. After four years of monitoring, δ2H and δ18O results indicate that the shallow groundwater samples are coincident with the reference meteoric water line, which implies these samples originate from rainwater. Surface water samples are consistent with water influenced by evaporation processes. Conversely, samples of produced waters exhibit a distinctly different isotope character compared to the shallow water samples. This suggests that there is no connection and mixing between surface and shallow aquifer with the deeper slurry injection zones. Stable isotope analysis has proven to be a successful groundwater monitoring technique and is an enabler for continued safe injection of slurry wastes into the deep subsurface. These efforts have been acknowledged by Kementrian Lingkungan Hidup dan Kehutanan (KLHK) of GOI experts as one of the most advance groundwater monitoring technologies currently in use in the Indonesian oil and gas industry.

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Surveillance is very important in managing a steamflood project. On the current surveillance plan, Temperature and steam ID logs are acquired on observation wells at least every year while CO log (oil saturation log or SO log) every 3 years. Based on those surveillance logs, a dynamic full field reservoir model is updated quarterly. Typically, a high depletion rate happens in a new steamflood area as a function of drainage activities and steamflood injection. Due to different acquisition time, there is a possibility of misalignment or information gaps between remaining oil maps (ie: net pay, average oil saturation or hydrocarbon pore thickness map) with steam chest map, for example a case of high remaining oil on high steam saturation interval. The methodology that is used to predict oil saturation log is neural network. In this neural network method, open hole observation wells logs (static reservoir log) such as vshale, porosity, water saturation effective, and pay non pay interval), dynamic reservoir logs as temperature, steam saturation, oil saturation, and acquisition time are used as input. A study case of a new steamflood area with 16 patterns of single reservoir target used 6 active observation wells and 15 complete logs sets (temperature, steam ID, and CO log), 19 incomplete logs sets (only temperature and steam ID) since 2014 to 2019. Those data were divided as follows ~80% of completed log set data for neural network training model and ~20% of completed log set data for testing the model. As the result of neural model testing, R2 is score 0.86 with RMS 5% oil saturation. In this testing step, oil saturation log prediction is compared to actual data. Only minor data that shows different oil saturation value and overall shape of oil saturation logs are match. This neural network model is then used for oil saturation log prediction in 19 incomplete log set. The oil saturation log prediction method can fill the gap of data to better describe the depletion process in a new steamflood area. This method also helps to align steam map and remaining oil to support reservoir management in a steamflood project.

No abstract