Multiattribute probabilistic neural network for near-surface field engineering application

Abstract

Unconfined compressive strength (UCS) is an important rock parameter required in the engineering design of structures built on top or within the interior of rock formations. In a site investigation project, UCS is typically obtained discretely (through point-to-point measurement) and interpolated. This method is less than optimal to resolve meter-scale UCS variations of heterogenous rock such as carbonate formations in which property changes occur within data spacing. We investigate the geotechnical application of multiattribute analysis based on near-surface reflection seismic data to probe rock formations for their strength attributes at meter-scale variability. Two Late Jurassic outcrops located in central Saudi Arabia serve as testing sites: the Hanifa Formation in Wadi Birk and the Jubaila Formation in Wadi Laban. The study uses core and 2D seismic profiles acquired in both sites, from which we constrain UCS, acoustic velocity, density, and gamma-ray values. A positive linear correlation between UCS and acoustic impedance along the core indicates that seismic attributes can be utilized as a method to laterally extrapolate the UCS away from the core location. Seismic colored inversion serves as input for neural network multiattribute analysis and is validated with a blind test. Results from data at both outcrop sites indicate a high degree of consistency with an absolute UCS error of approximately 5%. We also demonstrate the applicability of predicted UCS profiles to interpret mechanical stratigraphy and map lateral UCS heterogeneities. These findings provide a less expensive alternative to constrain UCS from limited core data on a field-scale site engineering project.

Acoustic impedance to outcrop: Presenting near-surface seismic data as a virtual outcrop in carbonate analog studies

“…The advent of the Fourth Industrial Revolution stimulates the development of artificial intelligence, where geoscience studies have seamlessly adapted machine learning applications into their methodology (Caté et al 2017;Chen and Schuster 2020;Liu et al 2020;Feng et al 2021;Ramdani et al 2021). The study of carbonate outcrop analogs is perhaps one of the branches of Geoscience that does not see an extensive application of machine learning despite its potential (Francis et al 2014;Kirsch et al 2018;Kwok et al 2018;Marques Junior et al 2020;Ramdani et al 2021). Converting seismic images into outcrops can be viewed as an image-to-image translation problem.…”

“…This study utilizes field outcrop datasets acquired by Ramdani et al (2021) and Ramdani et al (2022) over the 1 km x 1 km focus study area in Wadi Birk. Their datasets (Figure 2) include 2D near-surface seismic profiles, core, outcrop sedimentary sections, and drone-based digital outcrop model (DOM).…”

“…Their datasets (Figure 2) include 2D near-surface seismic profiles, core, outcrop sedimentary sections, and drone-based digital outcrop model (DOM). Ramdani et al (2021) recorded 636 m-long 2D seismic profiles, drilled a 50 m-long core, and performed colored inversion (Lancaster and Whitcombe 2000) to the seismic data. Ramdani et al (2022) constructed the DOM of the study area from > 6000 outcrop photos obtained from aerial drone surveys using fixed-wing, hexacopter, and quadcopter drones.…”

“…Similar to the field datasets, we utilize laboratory measurements of spectral gamma-ray, acoustic velocity, bulk density, and thin sections petrographic description of the core from Ramdani et al (2021). These laboratory measurements provide a calibration point for the seismic data, a continuous vertical depositional facies description of the interval of interest, and acoustic impedance (AI) properties.…”

“…Heterogeneity in reservoir characterization is non-uniform variations of reservoir parameters (porosity, permeability, seismic properties, and others) in space (Weber 1986). An interplay between sedimentary fabric, lateral and vertical depositional facies changes, overprinting diagenesis, and the distribution of fractures contribute to the 3D complexity and heterogeneity of fluid paths within carbonate reservoirs (Ehrenberg et al 2006a;Ehrenberg et al 2006b;Borgomano et al 2013;Vahrenkamp et al 2014;Vahrenkamp et al 2019;Chandra et al 2021).…”