Fifth EAGE Borehole Geology Workshop

This paper includes an evaluation of fracture detection between logging while drilling (LWD) and post-drilling wireline images. The imaging tools were electrical “micro resistivity” imagers that were used to image a sub-horizontal borehole drilled with water-based mud in a fractured carbonate reservoir. The obtained images were interpreted side by side and compared for fractures and other structural discontinuities. We observed that the wireline tool detects a few fractures and misses a great number of fractures detected by the LWD tool, which is of a slightly lower resolution. We recognize that this reflects the dynamics of reservoir-borehole fluids. In particular, two distinctive borehole conditions can explain the detection of reservoir open fractures. Firstly, overbalanced drilling forces water-based mud into fracture apertures to be detected as strong conductive features against less conductive or rather resistive lithofacies. Secondly, thick mud cake masks rock features as the tool sensors make only shallow resistivity measurements. The two conditions are in favor of using LWD imaging tools to detect, and therefore describe, reservoir fractures. This is despite the fact that LWD imaging tools are yet of a lower resolution and can encounter more drilling challenges than wireline imaging tools.

Evaluation of different failure modes and their time dependency based on repeat log acoustic borehole image data and geomechanical modeling for the deformed Lista formation at the Grane oil field in Norway.

Advancements in ultra-deep resistivity technology enabled the utilization of 3D inversion in real time. This in turn has enabled a wide variety of applications including azimuthal well placement, thin bed mapping, lithology identification at distance and 3D fault mapping. This abstract presents a summary of these advanced applications and real examples if their utilization.

Geothermal energy can be extracted and transformed into other forms of energy, acting as an important source of clean and renewable energy for several countries worldwide. This energy has a huge potential to be considered a promising, sustainable energy resource in Saudi Arabia that will contribute to the country’s ambitious vision of achieving net-zero emission goals. To achieve this, an in-depth understanding of the structural and geological architecture that controls the fluid circulation within potential geothermal sites is crucial. The exceptional situation of Saudi Arabia on tectonic plate boundaries along with favourable geological conditions, such as recent active tectonism and volcanism associated with the Red Sea rifting makes the country a host of many geothermal resources. Fracture density, orientation and connectivity are key determinants of geothermal reservoir permeability and fluid flow. Hence, a good understanding of fracture networks and fault zones is essential for any adequate, site-specific field development. This study aims to identify the main geological structures/structural lineaments in two geothermal fields (located in Al-Lith and Jizan) in western Saudi Arabia and analyze their densities and delineate areas of high permeability for further geothermal field development.

Deep Learning approaches are now widely and successfully applied in building data-driven models, for instance in relation to image analysis of thin sections, subsurface core images and seismic facies. The present work focuses on subsurface core images, which are arguably still underutilized by geoscientists, and targets the predictions of petrophysical properties (e.g. permeability, first methodology) and sedimentary structures (e.g. lamination, second methodology), which outcome respectively from laboratory analyses on core plugs and from core geological description. The Transfer Learning methodology applied to the VGG19 Convolutional Neural Network represents the basis of the two methodologies. The input data consist in slabbed core photos under white light conditions. Ultraviolet core photos, core-facies and core-gamma-ray can be added to petrophysical properties prediction to improve result’s quality. The output of the methodologies is a high-resolution curve of the predicted reservoir properties at the support volume of core plugs. As far as the first methodology is concerned, the resulting curve represents the optimal input for the upscaling process at any desired support volume (e.g., logs, grid-cell, seismic). The output of the second methodology can provide a further input to the first one or can feed the process of digital core modelling.

Carbonate rocks are complex in nature. The presence of heterogeneity in carbonates creates a challenge for the characterization of such rocks. Vugs are cavities in the rock created by dissolution or erosion and vuggy porosity affects the effective permeability in the reservoir. The identification of vuggy zones in the reservoir is important for the reservoir characterization. Cores and image logs are the main tools to identify and characterize the vugs in the reservoir. In this paper, two logging-while-drilling (LWD) technologies were run in the same reservoir to identify and characterize the vuggy porosity. Two different techniques were used to understand the vuggy system. The first LWD imager is a laterolog resistivity tool for water-based mud environments while the second was a dual physics imager (electromagnetic resistivity and ultrasonic) for oil-based mud systems. Both technologies provide ultra-high resolution and provide valuable information about the vugs in the reservoir. The vug density analysis is derived from advanced processing and interpretation of these high-resolution image logs. The results improved the understanding and prediction of the reservoir quality in carbonates.

Detailed understanding of the pore scale rock properties at micron resolution is essential for proper formation evaluation, which includes borehole log interpretation. Micro computed tomography (mCT) imaging becomes the standard tool to generate the framework, on which petrophysical models are applied. The advantage of petrophysical simulation is that the results are obtained with quick turnaround and at a lower cost compared to traditional experimental laboratory work.

This study presents a new multiscale approach to tackle the characterization of heterogeneous multimodal carbonate rocks using digital rock analysis based on mCT scanning. Multimodal carbonates are difficult to investigate because of a wide range of pore sizes, which runs the gamut from vugs (mm scale) down to micro-pores (sub-micron scale). We acquired mCT scans employing variable volumes of rock and at multiple resolutions for several carbonate samples. This resulted in a coarse- and a fine-scale 3D representations of the pores for each sample, on which the petrophysical modelling was conducted. The modelling results, particularly, the pore throat size distribution is a special feature that is used for petrophysical rock typing. Covering all pore sizes in a single mCT scan is not possible for the majority of carbonate formations.

The characterization of subsurface fractured reservoir is a challenging task, requiring a multiscale approach. The micro-fractures (from cm to meter length) are usually studied, for characteristics and distribution, from oriented cores and well logs, whereas faults (macro-fractures, more than hundred meters length) are interpreted and mapped from seismic data.

The study of meso-fractures (tens to hundred meters length), also defined sub-seismic fractures, is still arduous to be performed with direct tools. Recent technological innovations, mainly in Wireline (WLL) and Logging While Drilling (LWD) areas, allow the mapping of features tens of meters far from the wellbore. However, it is not a conventional approach, that requires a dedicated planning and a significant cost increase. On the other hand, meso-fractures are hard to detect and properly describe in the seismic volumes, due to the spatial resolution of the dataset and to the capacity to detect these features using seismic waves. To fill the gap between well data and conventional seismic data, it has been developed a workflow named Tectonic Fractures Recognition and Characterization (T\Frac). The integration of borehole image logs, borehole data able to detect meso-scale features within the workflow can support a more reliable characterization.

Automation in borehole geology while drilling has taken long strides in past few years with advanced algorithms, computer vision and machine learning. Borehole images and drill-cuttings that form the backbone of well-centric geological interpretation have witness deployment of features extraction, properties estimation etc. with good success. For borehole images, the high angle and horizontal wells still pose challenge in even beds-picking with larger sinusoids associated uncertainties, and work is under advanced stage on that front. Drilling related features (breakout/ induced fractures) can be extracted, and fracture extraction is being attempted as well with varying degree of success. Sedimentary facies automated interpretation has been attempted successfully on borehole images. Drill-cuttings automated interpretation complements the borehole geology interpretation while drilling. This digital transformation of geology while drilling portfolio has started to yield benefits in quick and accessible automated interpretation available to various stakeholders as drilling commences and efficient well-delivery is achieved on expected lines.