SPWLA Monthly Luncheon Meeting  

Thursday
May16 2019

PanAm Building- Suite 1600

601 Poydras St,
New Orleans, LA 70130

11:30 – 1:00 pm

Register and pay online or pay cash/check at the door.
RSVP: Elizabeth Tanis ([email protected]) Cost - $25 

An Unsupervised Learning Algorithm To Compute Fluid Volumes From
NMR T1-T2 Logs In Unconventional Reservoirs

Presented By: Lalitha Venkataramanan

Scientific Advisor at Schlumberger Doll Research

ABSTRACT

T1-T2 maps from wireline 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. However, these signatures depend not only on the fluid and the pore geometry but also the geometrical configuration of oil and water phases within the pore space. These volumes are usually calculated by using predetermined cutoff values obtained from analysis of laboratory data in the T1-T2 domain. However, these cutoff values are lithology dependent and a function of the unknown fluid properties. Thus, is desirable to have an automated algorithm that can compute fluid volumes from T1-T2 maps. In this paper, we describe an unsupervised learning algorithm to estimate the footprint of the different fluids in T1-T2 maps and subsequently compute their fluid volumes. Leveraging our knowledge of the physics of the relaxation processes and measurements of laboratory datasets, we propose a hierarchical clustering method consisting of the following steps. First, we use the signal to noise ratio in the data to obtain a rough estimate of the overall footprint of all the fluids. Second, assuming each point in T1-T2 space corresponds at most to one fluid, a non-negative matrix factorization technique is used to compute a footprint corresponding to the different fluids. A hierarchical clustering method is used to ensure that the footprint of each fluid is compact and connected in the T1-T2 domain. Subsampling of the maps is used to study the stability and compute the most likely number of fluids present. The final step consists of applying the mask corresponding to the different fluids to the measured T1-T2 maps to determine the fluid volumes. We demonstrate the application of this method on simulated datasets.

BIO: Lalitha Venkataramanan is a Scientific Advisor at Schlumberger Doll Research and Program Manager for Automated Log Interpretation is based in Cambridge, MA. She has worked in the field of measurement inversion and interpretation for over 20 years. Her research program focuses on inversion of multiple measurements to estimate rock and fluid properties. Her research interests include forward modeling and inversion of nuclear magnetic resonance, dielectric and optical measurements obtained from downhole and laboratory data as well as optimization, optimal experimental design and 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 over 10 granted patents and 15 pending patent applications, over 25 refereed Journal papers. She has given several invited presentations about her work at Universities and organized panel discussions and workshops for careers outside academia. She is a board member of SIAM industry committee.

New Orleans Chapter Open Positions: President Elect, Scholarship Committee Chair
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