EXTENSION OF SEISMIC INVERSION FROM ELASTIC TO ELECTRICAL PROPERTY FOR A COMPLETE RESERVOIR CHARACTERIZATION

The electromagnetic (EM) method provides opportunity in identifying and quantifying potential hydrocarbon reservoir although its time resolution is much lower than seismic data

EXTENSION OF SEISMIC INVERSION FROM ELASTIC TO ELECTRICAL PROPERTY FOR A COMPLETE RESERVOIR CHARACTERIZATION

The electromagnetic (EM) method provides opportunity in identifying and quantifying potential hydrocarbon reservoir although its time resolution is much lower than seismic data

3D and 4D inversion for rock and fluid properties using deep learning

This thesis focuses on estimating rock and fluid properties from the perspective of 3D and 4D seismic inversion. I developed two techniques that enable seamless integration of 3D and 4D seismic data. The first technique emphasises the estimation of porosity, Vclay, and hydrocarbon saturation directly from 3D seismic data using deep learning. Additionally, I propose an approach to enhance the lateral continuity of these estimated petrophysical properties. The products from this first technique are subsequently integrated into the 4D domain, leading to the development of the second technique that enables the inversion for reservoir pressure and saturation changes from 4D seismic data using deep learning. Both techniques involve the use of synthetic training datasets for network training, where the detailed processes for building realistic training datasets are presented. The first technique was tested across four fields with diverse deposition environments, covering meandering fluvial systems, fluvial estuaries, deepwater settings, and carbonate platforms. The second technique was applied to the meandering fluvial field with available 4D seismic data. This technique successfully distinguishes pressure effects from saturation-related effects in the 4D seismic response. It also highlights the importance of incorporating fluid flow information into the training dataset, enabling the network to capture the relationship between the superimposed effects of dynamic property changes and the corresponding 4D seismic response. Finally, I present a summary of the cost-benefit analysis of these developed techniques, demonstrating their ability to accelerate the inversion process in terms of turnaround time while providing robust solutions when applied to field applications

Estimating Petrophysical Properties Directly from Seismic: A Deep Learning Application to Carbonate Field for CO2 Storage Potential

Geological carbon capture and storage is vital for reducing carbon dioxide (CO2) emissions. Carbonate Field 1 in Luconia Province, offshore Sarawak is a potential CO2 storage site. Porosity and clay volume (Vclay) estimation from seismic provide valuable spatial and temporal information in characterizing reservoir distribution and overburden for assessing containment integrity and storage capacity. A deep learning inversion method for simultaneous estimation of porosity and Vclay was applied and tested in Carbonate Field 1. UNet architecture, chosen for its ability to preserve spatial resolution, processes post-stack seismic as input and petrophysical properties as outputs. Mean-squared error is implemented as the loss functions during the training on synthetic dataset. We use facies-based geostatistical simulation to generate 1D synthetic petrophysical logs. The petrophysical properties are linked to elastic properties through a carbonate rock physics model and fluid substitution using Gassmann's equation. The computed elastic properties are then used to calculate the angle dependent reflectivities (0°-30°) using the full Zoeppritz equations. The reflectivities are convolved with possible source wavelets derived from the seismic post-stack to generate the synthetic seismograms. These synthetic seismograms are then averaged to obtain a single noiseless synthetic seismic data, prior to addition of estimated field noise. 40,000 realizations of 1D synthetic datasets are generated for the training dataset. An equal distribution of 4000 realizations is allocated for both validation and testing datasets. Each 1D synthetic dataset consists of 128 samples, with a sampling rate of 4ms. The trained network model is applied to the testing data which has not been seen by the network during the training. Using Pearson correlation coefficient (cc) as the metric to evaluate the prediction performance, the model provides promising result (cc>0.80) for the estimated porosity and Vclay when evaluated on the testing data. Application on the field dataset of Carbonate Field 1 demonstrates satisfactory prediction performance, with cc value exceeding 0.65 and 0.85 for both the estimated porosity and Vclay respectively. Low (<0.2) and high (>0.5) estimated Vclay content is interpreted as carbonate and shale respectively. The findings allow interpreter to characterize the heterogeneity of carbonate depositional facies and quality by integrating the estimated porosity and Vclay for CO2 storage planning. The process of estimating the porosity and Vclay directly from seismic using the deep learning approach takes one week, which include training data preparation and implementation

The TB 3.1 To TB 3.7 Sequence Stratigraphy and Structural Developments of the West Baram Delta Basin, Offshore Sarawak, East Malaysia

AbstractThe West Baram Delta (WBD) basin is a structurally complex region with an abundance of hydrocarbon that has been produced and yet to be discovered. Within the basin, there is a drastic increase of sedimentary thickness occurred across the growth fault, contributed to major challenges for the sequence stratihgraphic framework correlation to be established throughout the basin. Understanding the growth fault development in terms of age-based within the region is critical for better accuracy in reservoir correlation, reservoir distribution and structural trap analyses. 3D seismic mega-merge of the West Baram Delta was used to interpret the third order Tejas B (TB) stratigraphic sequences. From the structure maps of the maximum flooding surfaces (MFS) and sequence boundary (SB), thickness maps were generated for the system tracts of the corresponding sequence, mainly the highstand and transgressive system tracts. Then, structural restoration using a method of layer back stripping and fault blocks shifting were conducted to study the depositional and structural evolution of the basin. The Late Miocene to Late Pliocene sequence and structural developments of the basin were mainly controlled by growth faulting activities are divided into seven stages: 1) WBD TB3.1 (~10.6Ma-~8.5Ma), 2) WBD TB3.2 (~8.5Ma-~6.7Ma), 3) WBD TB3.3 (~6.7Ma–~5.6Ma), 4) WBD TB3.4 (~ 5.6Ma-~4.2Ma), 5) WBD TB3.5 (~ 4.2Ma-~3.8Ma) 6) WBD TB3.6 (~3.8Ma-~3.0Ma) and 7) WBD TB3.7 (~3.0Ma-~1.9Ma) sequences. The high sediment supply rate is believed to provide conducive mechanisms for the gravity-induced syn-depositional growth faults to be initiated, which observed from WBD TB3.1 until WBD TB3.4. The growth faults in the basin were developed stage by stage from the south (landward) to the north (basinward) driven by the progradation of shoreface and delta sedimentation. The Northwest-Southeast wrench-induced compression which happened in Pliocene to Quaternary has caused basin inversion in the basin, where the trending of the fold axes is in the Northeast-Southwest orientation. The wrench-induced compression deformation was prominent at the proximal part of the basin, where its deformation extends distally down to the Baram field. The deformation developed the anticlinal features and faulting within this region. The intensity of the wrench-induced deformation decreases basinward, which is the reason why beyond the Baronia field, the deformation is less prominent. The distal part of the basin is mainly controlled by the gravity-induced syn-depositional growth faults tectonic style since the wrenching is not prominent. The seven third-order depositional sequences established as WBD TB3.1 to WBD TB3.7 sequences with a complex growth-faulted structure development in the West Baram Delta give a new insight of understanding the depositional and structural evolution through time which may lead to a better stratigraphic correlation and hydrocarbon trap analyses at the field scale.</jats:p

Cobalt coated optical fiber in distributed optical fiber sensing for effective tracking and monitoring of magnetic nanoparticles in the reservoir

Enhanced and Improved Oil Recovery (EOR/IOR) projects critically depend on effective tracking and monitoring of fluid mobility in the reservoir for optimal operation. Traditional fluid tracking methods often struggle with low spatial resolution and fail to accurately monitor low-contrast fluids in complex reservoirs, leading to gaps in understanding fluid movement. This paper introduces an innovative subsurface monitoring approach for EOR/IOR that leverages magnetic nanoparticles (MNPs) combined with a cobalt-coated fiber optic (CoF) sensing system installed along the borehole. The CoF system is optimized for high magnetic sensitivity (10-5 T) and sub-meter spatial resolution, enabling precise detection of MNP movements within the reservoir. Our research includes a sensitivity analysis that identifies the ideal magnetic permeability (μ between 10 and 100) for effective MNP injection and laboratory-scale experiments in a sand column setup to validate magnetic modeling. The CoF sensors demonstrated a 30 % improvement in magnetic field sensitivity compared to nickel-coated fibers, while effectively distinguishing between magnetic field changes and minor temperature variations, with a tolerance threshold of ± 2°C. The results show that CoF sensors can reliably track MNPs, map fluid flow along the borehole, and provide continuous, real-time data on fluid dynamics. Additionally, the study addresses the impact of temperature fluctuations on sensor performance and proposes mitigation strategies. These findings suggest that this novel approach significantly enhances the accuracy and reliability of EOR/IOR monitoring, leading to improved reservoir management, operational efficiency, and oil recovery