First EAGE Workshop on Surface Logging
- Conference date: November 12-14, 2025
- Location: Paris, France
- Published: 12 November 2025
1 - 20 of 51 results
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Identify Sand Channels within the Heterogeneous Units of Burgan Reservoir
More LessAuthors S. Al-HazzaSummaryAs a result of the previous mentioned study, the path of well (A) was planned to be drilled parallel to the fault simultaneously it was targeting the isolated sand channel. Post drilling well (A) found to be a very successful well where the oil found in all the four layers of Burgan reservoir including the massive sands. The sands in BGSU were well developed compared to the offset well, that’s matching with the theory we built from the inversion volume which suggested an isolated sand body exist in the area. The sands of BGSU unit were fully saturated with oil and no OWC was seen. Good net oil columns were encountered in the massive units (BGSM and BGSL2) where in the surroundings and in the remaining oil column maps these units are supposed to be drained. The presence of the sealing fault in the area was the main reason behind holding this oil from moving updip. This successful integration of data and findings clearly illustrate how by thinking outside the box and step out of our comfort zone helped us to reveal new opportunities and added more oil to KOC oil tanks
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Overcoming PPP Challenges in Non-Shales Environments
More LessAuthors M. Badillo, A. Pereira and M. RomeroSummaryThis document presents techniques for supporting the prediction of pore pressure and fracture gradient in real-time within non/shale formations (such as sandstones and carbonates), where traditional shalebased methods are not applicable. Using SLB’s PreVue service along with LWD/MWD tools, drilling parameters, and gas logging data, the approach helps to interpret indicators like mud losses, fluid gains and specially connection gas peaks.
The document emphasizes the importance of properly adjusting monitoring graph scales to detect events that might otherwise go unnoticed. A standardized workflow and plotting template are proposed to aid real-time interpretation. This methodology has been successfully applied in the Gulf of Mexico projects, enhancing understanding of the pressure regime during drilling operations-even without direct measurements.
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Optimization of Real-Time Cuttings Description through Standardization and Automation
More LessAuthors G. Chirila, M. Mezghani, M. Sadah, M. Khidir and M. KhanSummaryOne of the challenges faced by oil companies is related to the lack of standardized cuttings descriptions. To mitigate this challenge, we have proposed, developed and implemented a customized software with the purpose of standardizing the previously generated cuttings descriptions. To facilitate standardization, a custom-built cuttings description software was developed from scratch to streamline processes and ensure consistency in data collection and reliable information for analysis. This software integrates a highly structured schema for cuttings description. The principal benefit of deploying the cuttings description software lies in its ability to produce all cuttings description data on a daily basis in a standardized, tabulated format. Another substantial advantage relates to real-time data correlation, whereby once a cuttings sample is described, the data can be integrated and shared on correlation software platforms, facilitating collaboration and analysis.
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From Byproduct to Business Driver: Real-Time Drill Cuttings Intelligence for Safer, Faster, and Smarter Subsurface Decisions
More LessAuthors D. Tonner, A. Liborius and A. SwansonSummaryThis study highlights the transformative value of automated drill cuttings analysis as a real-time geoscience tool, especially when downhole tools like MWD and LWD are limited by cost, failure, or extreme environments. By integrating robotics and elemental analysis techniques—namely XRF and LIBS—the system extracts geochemical and mechanical insights from surface-collected cuttings. The process involves automatic collection, cleaning, and pulverization of cuttings, followed by high-resolution elemental analysis. Outputs include modeled mineralogy, brittleness, TOC, elemental gamma ray (EGR), and mechanical proxies, with quality control through standards and barium contamination checks.
Machine learning models, trained using PCA and DFA, classify chemofacies with >90% accuracy. Results from over 864 wells demonstrate broad utility: EGR curves replaced failed MWD in the Haynesville and Eagle Ford; geosteering improved through accurate facies classification; redox proxies mapped TOC-rich intervals; silica spikes identified bit wear zones; clay shifts predicted overpressure hazards; and elemental trends helped map subsalt faults and Jurassic boundaries in the Gulf of Mexico.
This workflow significantly enhances subsurface understanding, reduces non-productive time (NPT), and enables data-driven decisions in real-time. Its field-proven success makes it valuable not only in hydrocarbon development but also in carbon storage and other subsurface applications.
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The Calcium-Strontium Signature: An Applied Method for Identifying the Pre-Alagoas Unconformity in Santos Basin, Brazil
More LessSummaryThe Itapema and Barra Velha formations are key carbonate reservoirs in the Pre-Salt section of Brazil’s Santos Basin. However, distinguishing the contact between these units during drilling operations is challenging due to the lack of clear diagnostic features in well logs and the fragmentary nature of cutting samples. This study introduces a geochemical approach using real-time X-ray fluorescence (XRF) analysis of calcium and strontium (Ca-Sr) in cutting samples to identify the Pré-Alagoas Unconformity, which separates the two formations. Results from 20 wells show that in 13 cases, the contact could be recognized by the divergence of Ca and Sr curves, with Sr levels increasing above the unconformity. This geochemical pattern reflects changes in paleoenvironmental conditions, notably increased salinity during the deposition of the Barra Velha Formation. The method’s reliability is enhanced by the integration of XRF with X-ray diffraction (XRD) analyses, although it may be affected by diagenetic processes such as dolomitization or contamination by barite. The Ca-Sr technique proves to be a useful, real-time tool for stratigraphic identification during drilling operations in Pre-Salt carbonates.
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Empirical Estimation of Sw from MudLog Gas, Viscosity, and GOR in Overbalanced, Normally Pressured Oil Reservoirs
More LessAuthors R.B. JohnsonSummaryThis study presents an empirically derived formula for estimating water saturation (Sw) in oil reservoirs using surface logging data. Specifically, Sw is proportional to both mud log total gas analysis (TGA) and reservoir gas-oil ratio (GOR), and inversely proportional to drilling mud viscosity in overbalanced drilling systems. The approach enables real-time screening of reservoir productivity and early evaluation of reservoir quality where wireline data is limited or unavailable.
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Unlocking Data Potential: Unleashing the Power of Advanced Mud Gas for Real-Time Reservoir Insights
More LessAuthors M.C. Bravo, S. Roblero, S. Donnadieu, F. Ungar, G. Yerkinkyzy and T. YangSummaryRecent advancements in mud gas logging have significantly enhanced its contribution to the analysis of reservoir fluids during drilling operations. The utilization of advanced mud gas technology has significantly enhanced the accuracy of gas data analysis, enabling better predictions of hydrocarbon presence and fluid properties, and presented as continuous logs along the well while drilling. The development of a machine learning method called Real-Time Fluid Identification (RFID), leverages a vast reservoir fluid database to predict fluid properties. This method has led to a marked increase in the use of advanced mud gas technology, particularly in development wells. Emphasizing the necessity of thorough data quality assessments and a strong quality control (QC) framework is crucial for achieving reliable predictions from machine learning models. Furthermore, the use of visualization techniques, such as Radar plots, crossplots and histograms, are presented as a means to effectively analyze and compare gas component ratios. Overall, the integration of competence to carry out a robust data quality review, machine learning with real-time logging data emerges as a crucial requirement for accurate reservoir fluid predictions, showcasing the transformative impact of these technologies on the mud gas logging industry.
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Leveraging Real-Time Data and Advanced Tools to overcome Reservoir challenges in Silurian Formations, Algeria
More LessAuthors Y. Boudiba, M. Pisharat, A. Allel, A. Abidli, H. Brag, A. Benosman, A. Salemkour, A. Boundaoui, I. Fornasier and A. BakourSummaryAlgeria’s Silurian formation in a western field poses significant challenges in reservoir characterization and production. These challenges include deep and unstable boreholes, heterogeneous reservoirs with low resistivity pay zones, and substantial uncertainties in determining fluid contacts. The instability and complexity of the reservoirs, combined with low-resistivity pay zones, make it difficult to accurately identify and characterize hydrocarbon-bearing intervals. Accurate determination of the gas/water contact is crucial for reserves estimation, completion design, and development planning but remains challenging due to geological and petrophysical complexities.
Addressing these challenges requires a multidisciplinary and coordinated strategy. Overcoming these issues involves optimizing drilling operations, utilizing advanced logging and interpretation techniques, and integrating multiple data sources. An effective approach includes using advanced mud gas (AMG) technology for early formation fluid characterization and fluid contact identification, followed by a wireline formation tester (WFT) for precise contact determination and formation water identification.
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From From Bubbles to Breakthroughs: Decoding Reservoir Fluid Dynamics for Breakthroughs in Norwegian Oil Field Management
More LessAuthors F. Ungar, M.C. Bravo, S.R. Nunez, S. Donnadieu, G. Yerkinkyzy and T. YangSummaryThis paper explores advanced approaches to reservoir fluid dynamics in Norwegian oil fields, emphasizing the use of cutting-edge technologies to optimize oil recovery and reservoir management. It highlights the challenges of interpreting fluid phase behavior during depletion and gas injection, particularly in depleted reservoirs with complex gas/oil contacts. The study underscores the transformative role of Real-Time Fluid Identification (RFID) technology, which improves accuracy in fluid type identification during drilling, enabling real-time adjustments and better decision-making.
Case studies demonstrate the application of RFID and Logging-While-Drilling (LWD) technologies to optimize well completions, enhance oil recovery, and resolve ambiguities in traditional petrophysical evaluations. For instance, RFID helped identify oil zones and gas-oil contacts, increasing production efficiency. The paper concludes that integrating advanced data acquisition methods, machine learning, and interdisciplinary collaboration is essential to improving hydrocarbon recovery, extending the economic viability of mature fields, and ensuring efficient reservoir management.
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Machine Learning for Enhanced Insights into Flow Potential in Overburden
More LessSummaryPorosity and permeability are important parameters reflecting flow potential in subsurface. In Standardized practices quantify these parameters via core analysis, well logging, and well testing. Nonetheless, these methods are generally costly and mainly applied to reservoir sections. The emphasis on the identification of flow potential in overburden sections is commensurate with concerns about drilling safety and efficient well plugging operations. The study aims to explore an alternative method to quantifying the abovementioned parameters, specifically porosity, in the overburden sections. By leveraging our in-house database and machine learning (ML) techniques, we established a workflow that could yield a machine learning model for estimating the porosity in the overburden sections. We gathered data for 23 wells from the Norwegian Sea with similar geological properties. After preprocessing, we divided the data into training and testing datasets, with the training dataset being input into the SparkBeyond discovery platform for feature engineering. Through several iterations, we refined our ML model for further testing. Our findings exhibit the significant potential of utilizing ML to quantify the parameters and lay the groundwork for future research in this domain.
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Real Time Formation Fluid Characterization with Integration of Gas While Drilling Evaluation Techniques in Deepwater Suriname
More LessSummaryAccurate reservoir characterization is vital for successful exploration wells, but traditional evaluation methods like wireline logging and well testing can be slow and costly. To tackle these issues, a new approach was applied in a deepwater exploration well in the Guyana-Suriname basin, focusing on Campanian clastic reservoirs. This method uses advanced gas data to enable early fluid characterization during drilling. The integration of gas analysis with Logging While Drilling (LWD) data enhances the understanding of reservoir fluids and optimizes wireline log acquisition. The analysis revealed a lower Gas-Oil Ratio (GOR) oil in the shallow reservoir compared to the deeper reservoir. The results from Gas While Drilling (GWD) analysis helped to optimize wireline formation pressure data acquisition: pretests were attempted mainly where gas peaks indicated hydrocarbon zones. In conclusion, the application of Gas While Drilling (GWD) analysis has proven highly reliable for gaining early insights into hydrocarbon types in Campanian reservoirs in Guyana-Suriname basin. It allows for precise targeting of pressure testing and sampling at identified hydrocarbon intervals. Additionally, GWD helps reduce uncertainties in log interpretation of potential hydrocarbon zones, increasing the chances of successfully acquiring pressure data and samples from specific locations of interest.
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Chemo-stratigraphical Characterization in the Upper Eocene, Southeastern Gulf of Mexico: Insights from XRF-XRD Integration
More LessAuthors C.E. Lugo Garcia, A. Shoeibi, M.C. Sandu and A. GuglielmettiSummaryThe study area is located at Block #26 within the Salinas Basin, specifically in Deep Water at the southeastern Gulf of Mexico, where geological complexities in the Upper Eocene formations were observed. In this stage, the region has a distinctive geological history, marked by the interaction between the Caribbean and the southern Gulf of Mexico. The main challenges faced in the region were caused by sediment provenance, diagenesis, authigenic mineral formation, and hydrothermal alteration.
The methodology used a multi-faceted approach, integrating elemental ratios from XRF analysis, mineralogical identification through XRD, and chemostratigraphical evaluation. Our study emphasises a robust Quality Control framework during sample acquisition to prevent mixing and ensure accurate depth allocation. Our research monitored the potential contribution of radioactive minerals in non-shaly rocks, particularly in high Th, U, and K₂O values, a critical limitation associated with downhole GR logs. The results reveal a distinctive signature in the elemental ratios, with an increase in MgO, Cr, Ni, and Co, coupled with a depletion in K₂O, Al₂O₃, and CaO. Mineralogical shifts identified through XRD have confirmed a marked decrease in the Total Clays and Total Carbonates, along with a rise in Quartz, Feldspars Minerals, and the occurrence of Enstatite and Chrysotile.
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Utilizing Torque and Drag Predictive Modeling for Safe and Efficient Drilling
More LessAuthors D. MurraySummaryThis abstract discusses a case study on the use of predictive torque and drag modeling to prevent drilling hazards, ensuring safer and more efficient operations. The study focused on drilling a production well through a challenging formation with high pressures and problematic lithology. Torque and drag modeling emerged as a crucial tool for enhancing drilling performance and mitigating stuck pipe risks. By leveraging advanced computational techniques and real-time data, engineers could predict, monitor, and manage mechanical forces on the drill string. The study highlighted the importance of continuous monitoring of the friction coefficient, which should not exceed 0.4 to prevent stuck pipe incidents. At a depth of 1850 meters, the friction coefficient exceeded this threshold, resulting in a stuck pipe incident. Remedial actions involved circulating drilling fluids to stabilize the wellbore, allowing drilling to resume. The study underscored the effectiveness of torque and drag modeling in identifying and mitigating drilling risks, while acknowledging limitations related to input data accuracy and complex geological conditions. Overall, the case study demonstrated the importance of proactive management of friction coefficients to ensure safe and efficient drilling, particularly in challenging formations.
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Cost-Effective Fracture Detection in southern Kuwait: Real-Time Mapping with Surface Logging to Optimize Acid Fracturing
More LessAuthors O.I. Klinger Catano, A. Ranson, J. Azobu, J. Fernandes and A. ShoeibiSummaryIn southern Kuwait, a complex carbonate reservoir within the Lower Cretaceous formation has been characterized by micritic and wackestone facies with variable dolomitization, resulting in heterogeneous porosity and permeability. Its compartmentalized nature—divided by impermeable barriers such as tight carbonates or shale—poses challenges for hydrocarbon extraction ( Khan et al., 2013 ). To enhance production, horizontal drilling and acid fracturing are employed to connect isolated reservoir sections. Effective fracturing depends on the accurate identification of open natural fractures, which are typically detected using expensive wireline logging tools, such as formation micro-imagers or acoustic logs. This study presents a cost-effective alternative: using surface logging to monitor drilling fluid dynamics through a high-accuracy sensor. By analyzing micro-losses in conjunction with drilling parameters (e.g., rate of penetration and weight on bit), this method can detect open fractures, induced fractures, or permeable zones in real time. Field trials conducted in South Kuwait demonstrated a strong correlation between the surface-logging-derived fracture indicators and wireline log data. This technique offers a cost-effective solution, improves drilling efficiency, improve drilling safety, and supports more targeted completion designs—potentially enhancing production outcomes in a tight carbonate reservoir by optimizing acid fracturing stage placement.
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Drill-Bit Metamorphism Generates Artificial Hydrogen: A Challenge for the New Energy Exploration
More LessAuthors D. Strapoc and L. GerbaudSummaryThis study was stimulated by mud gas logging observation of likely drill-bit metamorphism-generated H2, and need of unbiased H2 distribution along wells drilled in exploration for naturally occurring hydrogen. The proven existence of the DBM gas by-products can flag and de-risk crucial mud gas logs for critical subsequent sampling, testing and completions decisions during hydrocarbons and H2 exploration. These successful experiments pave the way for systematic investigation of the DBM process to a wide extent, as a cautionary tale about practices used during exploration for petroleum and natural hydrogen.
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Mud Gas Advisor: Enhancing Fluid Interpretation Confidence with Real-Time Analysis of Drilling and Environmental Conditions
More LessAuthors D. Strapoc, I. Fornasier, G. Cera, L. Cezard, B. Pontillart, J. Audroing, D. Lockyer and P. FerrandoSummarySurface formation evaluation (SFE) acts as a continuous geochemical log of the encountered fluids along a well and these data are crucial for making real-time decisions such as geosteering, optimizing downhole sampling and well testing programs. The goal of the Mud Gas Advisor (MGA) workflow is to provide a real-time Interpretation Confidence Index (ICI) and to suggest data corrections or hardware solutions to effectively manage various influencing factors. Different from hardware QC, MGA’s novelty is that it enriches the user with the knowledge of confidence level of mud gas interpretation of the downhole fluid type, regardless the type of hardware used and the changing impact of the drilling and environmental conditions. Hence, the ICI offers a new real-time continuous SFE industry standard for mud gas logging across all service levels.
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Digital Approach for Lost Circulation Management
More LessAuthors S. Sharma, J. Converset and F. Le BlaySummaryIn this paper we introduce a data-driven agile method specifically designed, implemented, and validated to yield actionable insights for more efficient and accurate responses to lost circulation events.
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Optimized Gel Permeation Chromatography Configuration for Characterizing Fluid Properties from Drill Cutting Extracts
More LessAuthors G. Yerkinkyzy, S. Chen, T. Yang, M. Erdman, E. Michael, J. Alicja and I. CutlerSummaryGiven the absence of universal calibration standards for GPC, careful selection and control of system parameters are essential to minimize measurement errors. Prior to initiating large-scale data acquisition, it was essential to optimize the GPC-UV methodology. In early system configuration, the void time was excessively long, approximately 54 minutes, before any signal was detected. The primary objective was to reduce elution time and establish optimal operating conditions. Extensive laboratory testing was conducted to refine the setup, including evaluation of parameters such as the number of columns, mobile solvent type, solvent flow rate, operating temperature, sample injection volume and amount. A total of sixty stock tank oil samples, with API gravities ranging from 11° to 50°, were analyzed alongside with their corresponding wet drill cutting samples, collected from various fields across the Norwegian Continental Shelf (NCS). After observing the fluid response from GPC, the associated cutting samples were compared against the fluid sample. And both data exhibits a remarkable degree of similarity, indicating a strong correlation between the fluid and cutting sample profiles.
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Modeling Degassing of Drilling Fluids During Surface Circulation
More LessAuthors M. Dercq, E. Colombel, I. Fornasier, E. Yarman, A. Di Daniel and V. LamySummaryThe objective of the article is to demonstrate that the relationship between gas concentration exiting the well (Gas OUT) and gas concentration entering the well (Gas IN) can be captured using an analytical model-based calibration. The calibrated model captures the degassing of drilling fluid as it traverses the surface equipment before injected back into the well. Effectively, the model can be used as a virtual sensor in case of failure of Gas OUT or Gas IN measurement, and more.
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AI-Driven Lithotype Classification and Reservoir Correlation Utilizing Drill Cuttings: A Mesozoic Case Study from Europe
More LessAuthors E. ConventiSummaryThis study demonstrates how the integration of AI-driven techniques can transform drill cuttings into a powerful tool for reservoir characterization. A proprietary workflow that builds a detailed lithotype model by combining X-ray fluorescence (XRF) elemental data and high-resolution image analysis was used to analyze 278 samples from two European wells that targeted Mesozoic reservoirs.
Standardized imaging in both white light and ultraviolet light was part of the laboratory workflow, and 32 elements were subjected to XRF analysis. RGB and YUV color information, as well as particle metrics, were extracted from the images. Combining XRF and Image data, the results were used to classify samples into silicarich or carbonate-rich domains (via Si/Ca ratio) and further subdivided into 14 lithotypes based on brightness values.
The identification of depositional cycles, patterns, and Gross Sedimentary Packages (GSPs) was made possible by this classification. For example in Well B, cleaner intervals in the middle GSP suggested higher reservoir quality, whereas dark-colored, clay-rich, and reducing conditions in the upper GSP may be advantageous for hydrocarbon preservation.
With the aim of improving well planning and reservoir management throughout the region, this workflow provides a quick, quantitative, and repeatable method for understanding depositional environments and refining geological models.
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