Abstract
Acquisition of reservoir information from exploration campaigns in offshore oil reservoirs is a continuous challenge in today's operations. Reservoir fluid properties and reservoir parameters characterization are fundamental for the accurate reservoir description for field planning and facilities design. With the aid of new technology, data of the highest quality can be obtained while the well is being drilled. This data is a key input to the development plans for the area.
For an exploration well in an offshore Trinidad and Tobago oil field, in a reservoir of mainly unconsolidated sandstones with medium oil, the main objective was to acquire early and quick identification of the oil prospect for planning appraisal wells. A wireline formation tester (WFT) dual-packer module was deployed to perform an interval pressure transient test (IPTT), also known as a mini-drillstem test (mini-DST), at the interval of interest for assessing key reservoir parameters such as vertical and horizontal permeability, damage skin, and reservoir pressure, among others, in the near-wellbore domain, in addition to fluid sampling.
Downhole fluid analysis (DFA) was performed to identify the reservoir fluid properties including oil and water fraction, fluid composition, gas/oil ratio, density, viscosity, fluorescence, reflectance, and resistivity at multiple depths in real time. Also, the real-time insitu fluid characterization allowed making decisions about where and when to take the samples in an optimal amount of time. Additionally, a single-probe wireline formation tester was used to take fluid samples and to obtain a single-point formation pressure, used for determining pressure gradient. DFA was combined with pressure profiles to improve the determination of zonal connectivity across the reservoir.
The combination of IPTT and real time DFA characterization was applied at multiple depths and resulted in an improved understanding of oil reservoir, as well as lessons learned about methodology and applications and recommendations for future operations.
A robust deep structured prediction model for petroleum reservoir characterization using pressure transient test data
