The operator wanted to identify and drill a pilot section in a prolific oil field with a strong gas cap and moderate water drive. Fluid contact uncertainties, reservoir compartmentalization, and structural dipping presented challenges. Because of declining production, a new greenfield campaign was required.
Modeling for reservoir mapping resistivity-based measurements highlighted the challenges to land the wells in the thin oil pay zone with degrading rock quality and uncertain oil/water contact depth. Comprehensive reservoir fluid characterization proved crucial for selecting the final landing position of the subsequent sidetrack of the production section.
The operator used SpectraSphere service to evaluate the distribution of fluids and hydraulic units within the reservoirs with FlairFlex service in parallel to add to the continuous hydrocarbon fluid fill while drilling, optimize the risk of differential sticking, and confirm SpectraSphere service operations.
Unpredictable reservoir conditions, fluid and rock interactions, and drilling operational limitations can affect logging tools. Integrating data from SpectraSphere service and FlairFlex service optimized results. FlairFlex service identified the fluid distribution within the reservoir, reduced the uncertainties observed in the petrophysical evaluation, and identified potential transition zones while drilling. The service generated fluid data that enhanced previous conventional petrophysical interpretation of the initially suggested landing interval.
SpectraSphere service identified compartments and fluid distribution in the reservoir. By confirming the oil/water transition and clean oil flow from the reservoir, SpectraSphere service confirmed the FlairFlex service observations.
Both services provided capabilities to enhance data and analysis provided by each technology. FlairFlex service confirmed the presence of oil and water in the transition zone, while SpectraSphere service qualified the movable phase through its high pump rate and high drawdown to minimize station time. FlairFlex service used an innovative and predictive machine learning (ML) workflow to compute the GOR and fluid composition of the live reservoir hydrocarbons.
Using the two tools in tandem increased the working envelopes while providing a complete picture of the reservoir. The integrated solution enabled the operator to optimize pumping station time, reduce risk caused by stationary time, and obtain producibility and movable fluid indications. By predicting fluid properties using AI and ML in high contamination, the combination supported well-landing and geosteering decisions.
