Abstract
A nuclear magnetic resonance (NMR) logging tool can provide important rock and fluid properties that are necessary for a reliable reservoir evaluation. Pore size distribution based on T2 relaxation time and resulting permeability are among those parameters that cannot be provided by conventional logging tools. For wells drilled before the 1990s and for many recent wells there is no NMR data available due to the tool availability and the logging cost, respectively. This study used a large database of combinable magnetic resonance (CMR) to assess the performance of several well-known machine learning (ML) methods to generate some of the NMR tool’s outputs for clastic rocks using typical well-logs as inputs. NMR tool’s outputs, such as clay bound water (CBW), irreducible pore fluid (known as bulk volume irreducible, BVI), producible fluid (known as the free fluid index, FFI), logarithmic mean of T2 relaxation time (T2LM), irreducible water saturation (Swirr), and permeability from Coates and SDR models were generated in this study. The well logs were collected from 14 wells of Western Australia (WA) within 3 offshore basins. About 80% of the data points were used for training and validation purposes and 20% of the whole data was kept as a blind set with no involvement in the training process to check the validity of the ML methods. The comparison of results shows that the Adaptive Boosting, known as AdaBoost model, has given the most impressive performance to predict CBW, FFI, permeability, T2LM, and SWirr for the blind set with R2 more than 0.9. The accuracy of the ML model for the blind dataset suggests that the approach can be used to generate NMR tool outputs with high accuracy.
Highlights
Well logging tools provide continuous series of subsurface information that can characterize the rock and fluid properties
Clay bond water (CBW) is a layer(s) of water molecules that cover the surface of clay minerals, occupy part of the pore spaces
The accumulation of negative charges attracts cations and water molecules on the surface of clay particles known as clay bound water (CBW)
Summary
Protons associated with clay-bound water (CBW) and fluid in small pores display short T2, whereas fluid in larger pores shows longer T2 relaxation times. A T2 cutoff can be designated to distinguish between CBW, irreducible pore fluids or pores containing bound water (known as bulk volume irreducible, BVI), and producible fluids (known as the free fluid index, FFI) [1,5,12]. Where BP = bin porosity; CBW = clay bound water; BVI = bulk volume irreducible; FFI = free fluid index; ∅e = effective porosity; ∅t = total porosity; and Swirr = irreducible water saturation. Where T2LM is the logarithmic mean of the T2 distribution, milliseconds; and “a” is a coefficient that depends on formation type and has to be determined through calibration with core porosity. Where T2LM is the logarithmic mean of the T2 distribution, milliseconds; and “a” is a coefficient that depends on formation type and has to be determined through calibration with core porosity. “a” is generally close to 4 for sandstone
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