Borehole Image Logs Research Articles

Masked-SwinUnet-powered microresistivity borehole image inpainting

Electrical borehole imaging tools acquire high-resolution microresistivity maps of the borehole wall, revealing critical sedimentary and structural features for reservoir characterization. However, these microresistivity borehole image logs (BHIs) often contain regularly spaced unmeasured intervals due to the intrinsic gaps between the measuring electrode pads, which hinders effective BHI interpretation. Traditional inpainting methods, such as multipoint statistics and morphological component analysis, require extensive iterative processing and often fail in complex formations. Therefore, we develop a deep neural network trained in a self-supervised manner to enhance the efficiency and accuracy of BHI inpainting. This network incorporates a masked image modeling algorithm into the Swin Transformer model, focusing computation exclusively on valid pixels to eliminate inpainting artifacts and boost efficiency. The new model, named Masked-SwinUnet, leverages the known ground truth at artificially masked gaps in the measured stripes during training. It has successfully achieved seamless inpainting on data acquired by Schlumberger's Fullbore Formation MicroImager from the Integrated Ocean Drilling Program, effectively restoring global sinusoidal features and local textures. This novel inpainting technique holds great potential for enhancing automatic BHI interpretation and reservoir characterization.

The importance of core for carbonate reservoir evaluation: a case study from the Barra Velha Formation, Santos Basin, Brazil

Abstract The lacustrine carbonates of the Barra Velha Formation are a prolific hydrocarbon reservoir in the Santos Basin, Brazil. In many fields, they comprise decimetre- to metre-scale cycles composed of laminated calcimudstones, spherulite and shrub-dominated facies. However, locally these cycles are replaced by decametre packages of re-worked shrub grainstone/rudstone/breccia and in situ shrub framestone with significant (>30°) depositional dips. The latter could be interpreted in several ways (e.g. fault-block highs, carbonate mounds), but the integration of seismic, borehole image log and whole-core datasets converge on a model of aggrading carbonate mounds which developed in or marginal to a lake setting. The core datasets in this study demonstrate a distinctive depositional fabric within the carbonate mounds. From a production geology standpoint, the crucial difference between mound- and cyclothem-dominated successions is their permeability architecture. Cyclothem-dominated intervals show prominent and laterally continuous, decimetre-scale vertical matrix permeability variations. Mound-dominated intervals lack fine-scale palaeo-horizontal layering and exhibit a greater prevalence of irregular, centimetre-scale conduits and higher vertical permeability. This difference can only be reliably characterized via the integration of whole-core samples with other datasets and has a significant quantified impact on sweep and production performance.

Automatic Geological Facies Analysis in Crust-Mantle Transition Zone

The Oman Drilling Project was conducted as a part of the International Continental Scientific Drilling Program (ICDP) from 2017 to 2018, and several boreholes, including four across the crust-mantle transition zone, were drilled in the program (Matter et al., 2019; Kelemen et al., 2020; Takazawa, 2021). A full suite of slim and conventional wireline logs, as well as high-resolution electrical borehole image and geochemical spectroscopy logs (Ellis and Singer, 2007; Liu, 2017), were acquired near the coring borehole. Full core samples were acquired from the coring boreholes, and various core analyses were conducted manually with significant time and effort to create a detailed core description. Identification of geological facies is crucial to understanding the complicated crust-mantle transition zone. Being able to achieve this facies recognition from available logging data using an automated method would optimize the operation cost and analysis time. This would be useful for the future scientific ultradeep ocean drilling Mohole to Mantle (M2M) project (Umino, 2015; Moe et al., 2018) planned by the International Ocean Discovery Program (IODP). We propose an automatic geological facies analysis (FaciesSpect) method using borehole images and other petrophysical log data on the Oman Drilling Project. Among the available logging data, borehole image (resistivity type with dynamic color scaling) and two log curves (Fe and Ca) from geochemical spectroscopy log data were selected for the automatic facies analysis. Fifteen clusters were classified from the selected log data using the proposed approach. The cluster distribution trend was consistent with cuttings and core lithologies and indicated three major lithology zones: dunite, gabbro, and harzburgite. Two automatic facies analysis methods, class-based machine learning and heterogeneous rock analysis, were performed to compare the results with those of FaciesSpect. These are well-established methods that can validate the newer FaciesSpect method result. The class results of the three different methods were compared. They are matched at major lithology change boundaries. FaciesSpect class results calibrated by core data could be matched not only with core lithology changes but also with texture changes, such as massive, layered, severely altered, deformed, and fractured, due to the advantage of using borehole image data as one form of input. In this case study, we applied the automatic facies analysis to a complicated scientific drilling well in the crust-mantle transition zone for the first time. The result successfully identified different lithologies, such as dunite and harzburgite, which have a high potential for hydrogen generation and are important resources for emissions-free renewable energy. We validated that the FaciesSpect method is useful for rapidly understanding the overall lithology and texture trends such as fractured, deformed, and massive intervals. The FaciesSpect method can also save analysis time and provide a fit-for-purpose result for different objectives beyond petroleum evaluation in a way that does not rely on the individual interpreter’s experiences. The high-resolution borehole image and geochemical spectroscopy logs are crucial inputs for automatic facies analysis in this study.

Correction of linear fracture density and error analysis using underground borehole data

Fracture data acquired from drill core and borehole image logs require corrections for the bias due to fracture orientation, that are usually achieved by the Terzaghi correction technique. Previous studies often approximate the wellbore as a one-dimensional scanline, assuming that the length of the core axis within the sampling range is equal to the scanline length. This study refers to the commonly used workflow as the original Terzaghi correction method which is known to perform poorly when the angle (θ) between the core axis and the fracture is small. To address this issue, we propose an extension of the Terzaghi correction method that also considers the core diameter and is a function of both the fractures and the host layer dipping angle. The new method resolves the fracture density problem by selecting a new direction for the scanline perpendicular to the fracture and calculating the projection length of the sampling space in this direction. The fracture spatial arrangements and observed number of sampled fractures mainly affect the performance of this new approach. However, possible errors in the new correction method pertaining to the equidistant fracture density should decrease with increasing number of sampled fractures. It is found that an acceptable correction range exists for equidistant fracture density, though, when the corrected density is not within the acceptable correction range, the observed sampled fractures will not be equal to the true observed value. This means the corrected fracture density would be in an unacceptable range of errors. Moreover, the new method can provide results within the acceptable range when the angle between the fracture and the core axis is less than 20°. At the same time, this new method is free from other disadvantages in the original Terzaghi correction method, particularly, when the ratio of layers’ thickness to the core diameter is low. Therefore, the improved approach presented here is especially applicable to thin layers (no more than two times the core diameter) and conditions where the angle between the fracture and the core axis is less than 20°, which can contribute to fracture density characterization in the subsurface.

Lithocodium mound identification using logging-while-drilling image log and quantified cutting analysis — Validation with analogues

Lithocodium mounds are early Cretaceous sedimentary structures described in the literature from outcrops, however, never described in the subsurface. The objective of this work is to identify and characterize Lithocodium mounds in the subsurface along a 25,000 ft horizontal well. Drill cuttings sampled at a 100 ft interval are observed in thin sections to define and quantify key sedimentary indicators (bioclasts, facies, and texture). Logging-while-drilling (LWD) gamma ray (GR), density, neutron, and resistivity logs are acquired along with the LWD high-resolution borehole image (BHI) log. Bedding dips from BHI data, interpreted along the horizontal well, enabled the reconstruction of the reservoir paleotopography. In particular, the alternation of dip azimuth combined with the facies interpretation from the thin sections supported the interpretation of eight distinct mound structures. An assessment of their overall geometry confirmed the mound shape to be subcircular, consistent with the subcircular geometries observed in Oman at the outcrop. The inferred dimensions of the mounds are comparable with the Aptian Lithocodium mounds in Oman (30–40 m), and their intermound organization resembles that of the Albian mounds in Texas. This work demonstrates the value of analyzing cuttings to complement image log interpretation and the value of outcrop analogues for interpreting sedimentary structures. For the first time, the subsurface identification and characterization of Lithocodium mounds and intermounds are achieved.

Fracture identification in shale reservoir using a deep learning method: Chang 7 reservoirs, Triassic Yanchang formation

Natural fractures in shale oil formations play a significant role in fluid flow channels. However, their identification can be difficult due to the problems of subtle log responses of fractures and impact of sedimentary cycles on logging curve. To address these issues, a fracture identification method (FRNN) based on deep learning is proposed. FRNN combines window sliding and bidirectional recurrent neural networks (BiLSTM). A large number of sequence data samples from logs are obtained through window sliding, denoted with fracture labels based on rock core observations or borehole image log interpretation. BiLSTM processes sequences bidirectionally to mitigate the impact of sedimentary cycles on well logging curves and amply log responses caused by fractures. The integration of the sliding process and BiLSTM endeavors to address the challenge of fracture identification, which is often marked by significant uncertainty and subtle log responses. The experiments employ a dataset from Triassic Yanchang Formation's Chang 7 reservoirs in Q Oilfield, China, which contains shales and sandstones formed in braided river delta lacustrine systems that act as oil reservoirs. The statistical analysis and blind well test show that the fracture identification by the proposed FRNN method is consistent with those of core descriptions. Furthermore, examination of wellbore cross-sections reveals a congruence between the anticipated vertical distribution of fractures and the influencing factors thereof, thus validating the efficacy of the proposed methodology. The findings of this study can provide assistance in subsurface fracture modeling and hydraulic fracturing operations, etc.

A lookback study of well performance using borehole image data

The main objective of this work is to understand the impact of fracture, stress, drilling direction, and other reservoir properties on the production performance of a horizontal well (HW) in the Karachaganak Field. Taking advantage of the 70 available borehole image logs helped extend the analysis beyond individual wells to a field scale evaluation. Three analysis methods were developed to progress with the study: a favored drilling direction map, a Reservoir Property Filter, and crossplotting for multiple reservoir properties to gauge well performance. Results showed linear, nonlinear, and multidimensional relationships. The findings will be used to guide future HW drilling optimization, support dynamic modeling, and improve the model’s predictability for effective reservoir management.

Expanding role of borehole image logs in reservoir fracture and heterogeneity characterization: A review

Expanding role of borehole image logs in reservoir fracture and heterogeneity characterization: A review

Use of resistivity and density borehole image logs to identify and distribute facies in the Pikka unit — A case study from the Nanushuk Formation, North Slope, Alaska

Over the past few decades, the North Slope of Alaska has yielded several major hydrocarbon discoveries in the deltaic topsets of the Brookian Nanushuk Formation. Together, the Nanushuk topsets and genetically related foreset and bottomset beds of the Torok Formation comprise part of a giant clinothem system that prograded across the Colville Foreland Basin during the lower Cretaceous (the Aptian through the Cenomanian). The Nanushuk Topset Play contains stratigraphically trapped hydrocarbons within multiple fairways trending roughly north to south along the basin’s extent. Inside the Pikka Unit, the Nanushuk Formation represents a shelf-edge delta and attached shoreface system, defined primarily from sedimentological interpretations of conventional cores integrated with 3D seismic reflection data and well-log motifs. In this study, we build a structural and stratigraphic framework for the Nanushuk reservoir across the Pikka Unit through integrated observations of the borehole image logs, seismic data, and core. In the described workflow, the boundaries delimiting dip zones interpreted in the wells are correlated to higher order stratigraphic cycles within the Nanushuk clinothem zone. These boundaries are observed in seismic profiles and provide the basis of the structural framework. In addition, core facies interpretations are calibrated to CT-scan data to establish each facies character and vertical distribution within the wells. In the absence of core data, core-calibrated borehole images are instrumental in guiding sedimentological and structural interpretations. This extrapolation is dependent on the image log quality and resolution. The calibrated facies interpretations are applied to wells without core data and guided by seismic amplitude interpretation to extrapolate reservoir presence and distribution across the area.

Advancing subsurface analysis: Integrating computer vision and deep learning for the near real-time interpretation of borehole image logs in the Illinois Basin-Decatur Project

The accurate quantification and mapping of subsurface natural fracture systems using borehole imaging logs are critical for the success of CO2 sequestration in geologic formations, optimization of engineered geothermal systems, and hydrocarbon production enhancement. However, traditional interpretation processes suffer from time-consuming procedures and human bias. To address these challenges and expedite fracture analysis, we investigated the application of integrated computer vision and DL workflows to automate image log analysis. Specifically, the design of our workflow was crafted to swiftly detect fractures and baffles by using actual electrical resistivity of borehole wall from microresistivity imaging device alongside their binary representation. This novel approach significantly reduces computational time while providing invaluable insights. By incorporating conventional logging and microseismic data, we present a regional subsurface natural fracture mapping technique. Through the minimization of human bias in image log analysis, our automated workflow achieves reduced fracture interpretation time and costs while ensuring robust and reproducible results. We demonstrated the efficacy of our approach by applying the workflow to the Illinois Basin-Decatur Project site. The automated workflow successfully identified major fractured zones, multiple baffles, and an interbedded layer with a high resolution of 0.01 ft or 0.12 in. (0.3 cm) and can be upscaled to any desired resolution. Validation through microseismic and image log interpretations allows for accurate and near-real-time mapping of fractures and baffles, significantly enhancing CO2 pressure forecasting and postinjection site care. Our approach stands out due to its robustness, consistency, and reduced computational cost compared with alternative feature extraction technologies. It presents exciting possibilities for advancing CO2 sequestration and engineered geothermal efforts by offering comprehensive and efficient fracture mapping solutions. This technology can contribute significantly to the optimization of CO2 sequestration projects, facilitating sustainable environmental practices, and combating climate change.

Fracture analysis in borehole images from BM-C-33 area, outer Campos Basin, Brazil

In the BM-C-33 area, divided into the Raia Manta and Raia Pintada development areas, reservoirs are arranged into three structural highs: Gávea, Seat, and Pão de Açúcar. These reservoirs consist of pre-salt limestones deposited on volcanic sequences and underwent complex diagenetic evolution. Successive post-depositional processes, including silicification, affected original mineral assemblage, modified pore textures, and caused intense fracturing. Based on borehole image logs (BHI), wireline data from four wells, and 2D and 3D seismic data, this study details natural fracture acoustic and resistivity properties. It also discusses the relationship of faults, fractures, and vugs with diagenetic and tectonic processes. The authors used the data to divide the pre-salt section (Cabiúnas and Macabu formations) into three informal stratigraphic units. The analysis of interpreted fractures within these units suggests that major fracturing occurred due to regional tectonic stress, with local aspects like structural positioning interfering. The results of the fracture analysis imply a direct relationship between fracturing and silicification. Additionally, fracture density, vug volume distribution, and the presence of dissolution features like enlarged fractures limited to specific units imply stratigraphic control on fluid percolation. Finally, the study examines structural particularities in BM-C-33 area that potentially impacted intensity and extension of diagenetic alterations.

Enhancing Reservoir Zonation through Triple Porosity System: A Case Study

Summary The Asmari-Jahrom reservoirs, located in southwest Iran, are recognized as one of the major fractured reservoirs in the world. Understanding the role of fractures in enhancing hydrocarbon flow and permeability is of utmost importance. In this study, petrophysical conventional logs [neutron porosity (NPHI), density (RHOB), sonic (DT), and gamma ray (GR)] and advanced image logs [formation microresistivity imaging (FMI)] were used to investigate the reservoir properties. The novelty of this study lies in the implementation of triple porosity on reservoir quality and identification of flow units in Asmari-Jahrom reservoirs using petrophysical and borehole image logs. By quantifying fracture and vuggy porosity and correlating them with velocity deviation log and fracture parameters [fracture aperture (VAH) and fracture density (VDC)], it was demonstrated that fracture porosity is directly related to VAH. High peaks were observed in fracture parameters, particularly in VAH diagrams where the velocity deviation log was negative and low. Total porosity from density logs was found to match secondary porosity from petrophysical logs, validating FMI results. However, FMI log resolution was higher, enabling clearer identification of fracture porosity peaks. The velocity deviation log indicated that the predominant type of porosity in the reservoir was matrix (primary) porosity. However, fracture and vuggy porosity were also observed in certain zones. Based on indirect evidence such as drilling mud loss, porosity type (matrix, fracture, and vuggy), porosity amount, and oil saturation, 18 zones were identified to determine quality zones with appropriate reservoir quality. Asmari-Jahrum reservoirs were found to possess high storage and flow capacity. The presence of multiple fracture types, especially longitudinal fractures, contributed to the development of secondary porosity and enhanced flow unit quality. Despite their complexity, these fractured carbonate reservoirs were analyzed comprehensively through integrated petrophysical and FMI log interpretation, enabling optimized reservoir performance and facilitating hydrocarbon production.

Technology Focus: Formation Evaluation (February 2024)

The scientific research process begins as one tries to find explanations for a phenomenon. We make observations, define the problem statement, and review the existing domains of research that could be used. Another approach is to explore theoretical problems, those that are purely conceptual at present but provide a solution when a related observation is made in the future. Though these approaches sound isolated, both are part of characterizing uncertainty, and uncertainty comes in all scales and dimensions. This challenges us to learn at all scales possible, from the fume hoods in the laboratory to magnificently exposed outcrops and through deep narrow boreholes that drill through subsurface reservoirs. The combined efforts often convert learnings to actionable intelligence. At a smaller scale, porosity and permeability are probably the two most-studied rock properties among those that have meaningful implications for hydrocarbon reservoirs. Paper SPE 216856 considers machine-learning (ML) methods for classifying reservoir texture at a microscale. Borehole-image logs long have been used to obtain a picture of subsurface reservoirs. Unfortunately, a majority of the observations are qualitative. Quantifying these features faces the challenge of continuity, upscaling, and regional correlation. As we explore the latitude of ML-based applications, the use of these techniques for quantifying image logs becomes very relevant. The authors of that paper contribute to quantifying textural features at a “fume-hood scale” and develop a work flow with the potential for estimating properties such as porosity and permeability from a different domain of reservoir characterization. I often wonder how much the domain on formation evaluation encompasses. While geoscience-driven reservoir characterization is a big part of it, how reservoirs change over time also is a complementary observation. Paper URTeC 3864861 discusses various aspects of geomechanical changes that a hydraulically fractured reservoir goes through during its life cycle. The authors here study the relationship between measured strain from the fiber-optic sensors and wellhead pressure. Research like this could be extended to predicting production profiles and estimating recovery factors, which are important considerations in designing a stimulation program for sustaining production, maximizing recovery, and improving financial matrices for the capital program. I believe information could be categorized as learning, knowledge, and intelligence. Any scientific process starts with set of careful observations bound by an envelope of hypotheses. This is learning. Learning, which could be verified by predictable and repeatable outcomes from carefully designed experiments of complementing domains, becomes knowledge. Actionable knowledge, which then could be used to alter an outcome or a process, becomes intelligence. Paper URTeC 3871303 discusses a development strategy in a restrictive development unit with an existing parent well. Here, considerations are heavily weighted toward optimizing both interwell spacing and capital efficiency. The search for answers to a problem like this must seek guidance from a variable-scale experiment. The study here establishes the big picture with the structural elements of the basin that could restrict both the continuity of the reservoir and the nature of the producible fluid. With this framework, the model is then set to iterate from several different perspectives. Potential interwell communications are explored by measuring fracture-driven interactions (FDIs) and quantifying stimulated reservoir volume. What is impressive here is the different domains from which the authors seek answers. Direct observations from acoustic-fiber measurements for FDI and the geochemistry of produced fluids for identifying unique signatures from vertically separated formations are individual domains that seek the same answers in various scales. The study recommends the optimal spacing between wells and a stimulation design that minimizes well interference, reduces competition for resources between wells, and avoids overcapitalizing the program. This is how knowledge transforms into intelligence. I hope the readers appreciate the scales of characterization in these three papers. As a student of geology, I have always been fascinated by the concept of scale and its relation to the domains of science that we deal with. Unlike general relativity and quantum mechanics, most geologic phenomena are observed in all scales. It is just the uncertainty that needs to be quantified.

Application of GAN to Resolution Enhancement of LWD Real-Time Image Logs to Support Decision Making

In the current scenario of project management, where the agility and optimization of operations have been prioritized, the practice of logging while drilling (LWD) has gained space compared to traditional wireline logging. In theory, acquiring quality petrophysical properties during drilling brings greater agility in decision making about completion and optimizes operation costs. However, regarding borehole image logs, due to limitations in transmission capacity, the actual available data in real time contain about 50% (for resistivity images) of the full azimuth information, being insufficient for the identification of critical geological structures capable of impacting the communication between production or injection zones or the quality of cementation, such as fractures, caves, and geomechanical collapse zones. The tool’s memory data with the full information may take a few days after the end of drilling to be delivered by the service company, which in some cases is not enough for fast decision making regarding completion. In this work, we tested models based on generative adversarial neural networks (GANs) to reconstruct the complete memory data based on real-time input. As in conventional GAN schemes, a generator is trained to receive a real-time input and create a “memory-like” image, while a discriminator is trained to tell real and fake images apart. To regularize the convergence of training, we used an architecture known in the literature as CycleGAN, where another generator-discriminator pair is trained simultaneously to do the reverse process, recreating the real-time data. Variations of the training process and data sets were used to generate different CycleGAN models. They were trained using logs of presalt reservoirs in Buzios Field, and performance was assessed on logging intervals not seen by the algorithms during training. The results achieved so far have been very promising, as in certain intervals, resultant models were able to capture the presence of fractures and caves. This methodology represents a way of circumventing telemetry limitations, where missing information is added indirectly to the real-time data as the artificial intelligence (AI) algorithm learns the main characteristics of a field/reservoir. Therefore, previous knowledge from the field can be used to continuously optimize future operations, efficiently incorporating the available database into the workflow of petrophysicists for the recognition of geological and geomechanical structures in time to support decision making in completion operations.

New insights into hypogenic non-matrix dissolution mega features driving subsurface fluid movement within the Arab Formation, Saudi Arabia

This paper reports the results of a large integrated study into the classification, distribution and significance of hypogenic non-matrix large-scale dissolution features present in the Arab Formation of Saudi Arabia. The investigation of these features was based primarily on extensive core, wireline caliper, wireline/log while drilling resistivity borehole image logs supported by encountered total lost circulation of drilling fluids and production log data. Dissolution features observed by this study were classified into three main categories: touching vugs, ramiform voids, and cavities. This study is the first to successfully quantify the presence and extent of these non-matrix intervals in this formation, which has greatly improved the 3D modelling efforts. Over 8100 feet of large-scale dissolution features were intersected in both vertical and horizontal wells. The hypogenic genesis of the large-scale dissolution features pre-dates replacement dolomitization in the area. Given the extensive data reviewed, the presented results give insight into the importance of the large-scale dissolution mega features as they are strongly related to total loss of mud circulation during drilling, and will have implications for well-to-well short-cutting during production and impact oil storage capacity.

Curvature analysis and its correlation with faults and fractures in presalt carbonates, Santos Basin, Brazil

Presalt lacustrine carbonate reservoirs in the Santos Basin, Brazil, can have a high degree of fracturing. Properly characterizing such properties is important due to their influence on the porosity–permeability system. It may play a key role in the so-called excess permeability features and the flow behaviour, especially in the Aptian Alagoas Stage. Therefore, 3D characterization of the fracture system improves the reservoir model. For this purpose, correctly identifying the fault and fracture zones in seismic data is crucial. This task is generally performed manually and guided by amplitude interpretation or geometric seismic attributes. However, due to the seismic resolution of the pre-salt seismic datasets, many faults and fractured zones are not identified in the seismic amplitude domain or by standard geometric attributes such as coherence. Improving the resolution of seismic data and its geometric attributes can positively impact the delineation of fractured zones, structural lineaments, and their connectivity. Curvature (k) attributes have a high potential to reveal vertical to subvertical subseismic-scale structures, which is the main type of strain generated from the tectonic style in the Pre-salt rocks. Among the curvature attributes, the most-positive (k1) and the most-negative (k2) attributes are some of the most commonly used. The present study evaluates the use of seismic inverse Q-filter and three different dip estimation techniques based on Fast Fourier Transform (FFT), Principal Component Analysis (PCA), and Phase Gradient (PG). It also adds two postfiltering techniques (median and recursive Gaussian filters) to obtain a consistent most-positive curvature. We then analyse two analytical models that correlate the volumetric fracture intensity, measured on Borehole Image Logs (BHI), with the curvature. Then, we perform a comprehensive study to understand the role of seismic-scale strain (translated into curvature) and mechanical stratigraphy in the development of fractures in the presalt, where we address that in addition to seismic-scale deformation, the contents of silica, clay, and organic matter are also relevant variables. These results can help to guide future studies of fracture characterization to prospect naturally fractured reservoirs or to estimate fracture intensity for dual-porosity reservoir modelling on the Santos presalt carbonates.

Pre-salt carbonate cyclicity and depositional environment: NMR petrophysics and Markov cyclicity of lacustrine acoustic facies (Santos Basin, Brazil)

The Pre-salt strata are idealized as a lake-dominated breakup depositional sequence recorded between the South American and African continental margins to which there is no present-day analog. Most of the complexities of the Pre-salt reservoir rely on addressing carbonate cyclicities at different scales using limited cored intervals where well-log and seismic correlation is still unclear. In this case, the application of borehole image logs (BHI) and nuclear magnetic resonance (NMR) techniques allows not only the scale correlation and extensive data acquisition but also to establish a more robust relationship between acoustic facies and their petrophysical significance. For that, this work combines BHI and NMR data to propose a feasible scheme of depositional acoustic facies for the Barra Velha Formation in the Santos Basin. To verify the statistical significance of the acoustic facies’ vertical organization, this work applied the Markov chain analysis along a c. 500 m stratigraphic interval, deriving four carbonate cycles associated with shallowing and reworking lacustrine cyclicities. Abrupt changes in cyclicity patterns were adopted as stratigraphic markers to subdivide the succession into lowermost, intermediate, and uppermost intervals that record the depositional history of deep, shallow, and emergent lake environments, respectively. By comparing the thickness variation of the Markovian cycles (i.e., accommodation space) with the seismic onlap curve variation (i.e., lake level) throughout the Pre-salt interval, a larger-scale superimposed cycle was unraveled, suggesting that accommodation space of the studied Pre-salt paleolake was likely controlled by fluctuations in the lake level. Finally, this work aimed to explore the applicability of diagnostic criteria for the description and interpretation of depositional acoustic facies, their petrophysical significance, and cyclicity patterns in the Barra Velha Formation.

How to estimate the 3D stress state for a deep geological repository

Abstract. When stresses yield a critical value, rock breaks or pre-existing faults are reactivated, generating pathways for fluid migration. Thus, the contemporary undisturbed stress state is a key parameter for assessing the stability of deep geological repositories and for quantifying whether stress changes induced by sub-surface constructions and storage of heat-generating waste lead to a critical state. The prediction of the contemporary 3D stress state is achieved with geomechanical–numerical models that are based on the static geological models. These are based on 3D seismic imaging and borehole data and describe the 3D structure and distribution of rock properties. Furthermore, pointwise measurements of the stress state are needed to fit the model to these data. The workshop is divided into three parts. Each part will have a short introduction to the specific topic, followed by dedicated hands-on exercise in small groups and discussions of the exercise outcome and its implications. 1. How do we formally describe the stress field? Here you will learn that the stress field is different from other physical field quantities such as temperature or displacement. The latter can be described with a scalar or a vector at a point. In contrast, the stress state at a point is described with the so-called Cauchy stress tensor that has nine components. We will give a quick tour through this concept, starting from very basic physics, and explain why the tensor is needed. In particular, you will learn in the exercises that stress cannot be measured directly with most methods but that strain is observed, from which stress is inferred.2. How do we actually measure stress? We present a range of methods that determine individual components of the stress state. In the exercise you will analyse borehole image logs to identify borehole breakouts and drilling-induced tensile fractures at the borehole wall. These are used to derive the orientation of the minimum and maximum horizontal stresses. Furthermore, we will present methods for the estimation of stress magnitudes. These are essential data for the calibration of a 3D geomechanical model. We will give a brief overview of different methods and in the exercise on the analysis of mini-hydraulic fracture tests as well as on the sleeve-fracturing and sleeve re-opening methods.3. From a viewpoint to a 3D description. If rock properties in the sub-surface were homogenous and isotropic and were to follow a simple linear behaviour between stress and strain, we could estimate the stress state analytically. However, this is not the case, and we will discuss three questions. a. How many pointwise stress data are needed to efficiently constrain a 3D model?b. How do I know whether a data point taken from less than a cubic metre is a good representation of a large rock volume, i.e. a whole lithological layer?c. How can we quantify the uncertainties of the model stress state, and what is the typical range? You will use a case study in northern Switzerland to explore these questions.

Characterization of silicification and dissolution zones by integrating borehole image logs and core samples: a case study of a well from the Brazilian pre-salt

Precise knowledge of the spatial distribution patterns of non-matrix porosity zones and the establishment of the geological factors controlling their evolution are crucial for building more accurate carbonate reservoir models and improving hydrocarbon production. The occurrence of intervals affected by significant carbonate dissolution may result in drilling fluid loss and time-consuming drawbacks during well construction. Vug or cave-rich reservoirs may exhibit excess permeability and extremely high initial flow rates. Similar situations have been reported in exploration activities in the Brazilian pre-salt plays, where evidence of dissolution and other burial diagenetic processes, such as severe silicification and dolomitization, are common. In this study, we investigate evidence of major post-depositional changes in the lacustrine carbonate reservoirs of the Barra Velha Formation, which comprises the most prolific hydrocarbon play in Brazil. Using a comprehensive database consisting of both core samples and well-log data from a vertical well in the Santos Basin, we have characterized, at multiple scales, reservoir zones affected by silicification and carbonate dissolution. In addition, we have performed a petrophysical evaluation of the reservoir to understand the impact of such processes on porosity and permeability development. The results suggested an intimate relationship between silicification and dissolution processes, which can be associated with late fluid percolation under a deep burial flow regime. The occurrence of silicified and vuggy beds, associated with specific zones and lithofacies, indicates an important degree of stratigraphic control on fluid percolation and lateral migration. Furthermore, the presence of fractures at discrete stratigraphic levels has preferentially influenced the development of high-permeability zones, including metric-scale fracture-related conduits. This study contributes to the general knowledge of carbonate reservoirs affected by silicification and dissolution while providing support for the recognition of such processes in partially- or non-cored wells.

Computer vision techniques applied to automatic detection of sinusoids in borehole resistivity imaging – A comparison with the MSD method

In this research computer vision techniques are applied to borehole resistivity imaging in order to establish an alternative procedure to the mean square dip (MSD) processing. The MSD is regularly applied to detect sinusoids and dips automatically in borehole imaging and dipmeter logs. The present proposal is based on Gabor’s filters, morphological transformations, Hough’s transform, and clustering techniques. The MSD method and the computer vision proposal were tested in 1012 m of images, showing 7.986% of false positives for the MSD processing and 0.879% for the computer vision approach. This methodology tries to emulate the geologists behavior when they make image interpretation; instead of making correlations between resistivity curves like the MSD does. There are no special computer requirements, and it can be applied directly in the field for quick well-site dip results. This procedure can be easily integrated into log units and most commercial borehole-imaging processing software. The processing workflow was developed in python using standard libraries.