Improved fluid characterization in complex water flooded reservoirs with advanced LWD
Seminar Date: Sep 28 2017
Registration Opens: Sep 23 2017 - Sep 27 2017
Time: 11:30 AM - 01:00 PM (US CDT)
Admission/Registration Link: None
Meeting/Webinar Link: None
Contact: Bernd Ruehlicke ( Houston, TX, USA, SPWLA Houston Chapter)
Corresponding: firstname.lastname@example.orgFees: FREE
We present a new method to derive continuous reservoir fluids properties (saturation, salinity, density and hydrogen index) in a complex siliciclastic brownfield. The complexity of our study field lies in the uncertainty of formation salinity, as the water-flooded sands contain an unknown mixture of the connate brine and injected water. In such environments, one cannot simply assume a fixed salinity value when calculating saturation logs with equations that rely on a good knowledge of salinity. Formation evaluation is further complicated by low resistivity contrast between wet and pay sands, where high volume of salty irreducible water lowers resistivity in hydrocarbon-bearing sands. In this field, water production is a big concern for the operator. They would like to gain better understanding of the water flood encroachment to make smarter development plans in the future. To achieve this goal, they acquired advanced LWD logs in a number of development wells to characterize reservoir fluids. The use of LWD ensures that invasion of the drilling fluid into the reservoir sands is minimal. Thermal neutron capture cross-section (Sigma) is sensitive to chlorine in the reservoir fluids and rocks and can be used to distinguish between water and hydrocarbon in a salty water environment. Since both Sigma and resistivity are dictated by water saturation (Sw) and salinity together, we can use these two measurements to simultaneously solve for Sw and salinity at each depth in a non-linear least squares inversion routine. The resistivity-sigma workflow assumes total porosity is known but does not require a priori knowledge of salinity and outputs a continuous Sw and water salinity log that best honor the input Sigma and resistivity logs. With additional help from bulk density and hydrogen index (HI) logs, we can further derive fluid property logs of formation water and hydrocarbon using a similar technique. Water density, HI, Sigma and resistivity are written as functions of salinity, pressure and temperature. Similarly, hydrocarbon density, HI and Sigma are written as functions of its API gravity using Standing correlation for solution gas (Rs) and oil formation volume factor (Bo), pressure and temperature. After parameterizing the fluid components in this manner, we use a non-linear solver to invert the log response equations of bulk density, HI, Sigma and formation resistivity for water salinity, API gravity, volume of water (Vw) and volume of hydrocarbon (Vh) at each depth. The output of the resistivity-sigma-density-HI workflow is density, HI and Sigma logs of water and hydrocarbon respectively, in addition to total porosity (Vw+Vh), Sw and salinity. The quality of inversion output can be controlled by the new display charts of the water and hydrocarbon parametrization. The interpreted Sw logs in horizontal wells are consistent with the well production history. The interpretation also identifies changes in water flooding and provides a better understanding of the horizontal well production. The interpreted hydrocarbon density and viscosity logs are also consistent with measurements made on produced hydrocarbon samples at the surface.
Ting Li is a senior interpretation engineer with Schlumberger in Houston, TX. He began his career as a research engineer in 2006 at the petroleum engineering department of Stanford University. He joined Schlumberger-Doll Research in 2008 as a research engineer and spent 5 years working on nuclear spectroscopy, NMR and integrated interpretation of unconventional reservoirs. In 2013, he was transferred to Beijing, China as an associate domain champion of LWD petrophysics to support operations and technical sales of Schlumberger Drilling and Measurements. He received his Master of Science degree in computer science from University of New Mexico. He is a member of SPE and SPWLA.
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