Computing Fluid Volumes From T1-T2 Maps In Unconventional Reservoirs
Speaker: Lalitha Venkataramanan (Schlumberger-Doll Research)
Date: Wednesday, May 15th, 2019
Venue: MetaRock Laboratories
2703 Highway 6 S, Suite 280A, Houston, TX 77082
Time: 11:30 am – 01:00 pm
Regular Admission ($15)
Student and In Transition Professionals ($10)
Parking Info: Parking lot located in front of the building.
Please register by May 15th 2019 @ 12 pm to reserve lunch and pre-registration
Contact: Jeff Crawford
Corresponding: Jeffrey.Crawford@halliburton.com // 281-871-2168
T1-T2 maps from NMR logging tools show unique signatures for hydrocarbons such as bitumen and producible and bound oil and gas. Similarly, capillary and clay-bound water and water in larger pores have different signatures. These signatures depend on many factors: properties of the fluids (composition, viscosity), properties of the rock (pore geometry) and the geometrical configuration of fluid phases within the pore space. Unless the fluids have very distinct and non-overlapping signatures in the T1-T2 domain, it is challenging to visually separate the contribution from different fluids and estimate fluid volumes from T1-T2 maps. This problem was addressed by an automated unsupervised learning algorithm called blind source separation (BSS), wherein the NMR T1-T2 maps of an entire logged interval are factorized into two matrices: the first matrix contains the T1-T2 signatures of the different fluids and the second contains the corresponding volumes. This method has been shown to work well on multiple field data sets, where there was a sufficient dynamic range in the underlying volume fractions (Anand et al., 2016, 2017; Ortiz et al., 2017). In this presentation, we address two well-known limitations in the BSS algorithm. First, the algorithm assumes a dynamic range in the volume fractions. To ensure that there is a dynamic range in the data, the entire logged interval is considered in the matrix factorization. However, doing so mixes the effects in T1-T2 maps due to changes in rock properties with changes in fluid volumes. Second, it assumes that the number of sources (or fluids) is known a priori. This is a well-known ill-conditioned problem and is akin to model-order selection in the field of signal processing. We propose several modifications in the algorithm to address the above two limitations. First, we leverage the information that the NMR signature of fluid is expected to be connected in the T1-T2 domain. This expectation arises from the smoothness constraint imposed on the inversion algorithm used to compute the maps from the measured magnetization data as well as the underlying smooth distribution of the composition of crude oil. Second, we assume that each point in T1-T2 space corresponds at most to one fluid. Lastly, we propose a quantitative metric to guide the analyst in selecting the number of components. We demonstrate the application of this method on simulated datasets as well as field data sets from the Eagleford formation and Permian basin.
Lalitha Venkataramanan is a Scientific Advisor in the Applied Math and Data Analytics department at Schlumberger Doll Research, Boston. She manages a program on Automated Log Interpretation. Her interests include petrophysics, machine learning, mathematical modeling and inversion, optimization, probability, and stochastic processes. Trained as an Electrical Engineer, she obtained her M.S and Ph.D. degrees from Yale University in 1998. She has published over 25 research papers and holds more than 14 U.S. patents. She is currently an active member of SPWLA, SPE and a board member of the Society of Industrial and Applied Math (SIAM) industry committee.
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