Advancing Subsurface Imaging, Energy Transition and Digital Innovation

Polymer flooding can be of pivotal importance in improving oil recovery factors from carbonate reservoirs. Bio-based polymer floods have displayed excellent and cost-effective enhanced oil recovery (EOR) performance at laboratory-scale experiments. Nevertheless, despite these promising performance signs, many bio-based-polymer flooding options fail to progress outside the experimental stages. This paper’s thrust is to numerically assess the performance of an okra-based solution for polymer flooding across different scales. The numerical models aim to reproduce the results from laboratory core-flooding experiments that were previously published. Furthermore, this study conducts a sensitivity assessment examining the factors that significantly impact the flood’s outcome. The models are also used to explore oil displacement and the propagation of the okra-based polymer. The numerical model was built on CMG STARS using a one-dimensional (1D) approach to match lab-scale flooding experiments. The laboratory examination was designed to be applied at reservoir conditions in western Kazakhstan. CMOST was used for history matching the simulation outputs with the lab-scale oil production volumes. The procedure honored the upper and lower range limits of the following parameters: polymer viscosity and concentration, water and polymer injection rates, along with a relative permeability model for both for water flooding (WF) and polymer flooding (PF) conditions. A sub-model experiment design (DoE) was generated explicitly using the Latin hypercube sampling (LHS) method. The numerical simulation results showed that the laboratory-obtained final oil recovery numbers for WF exceeded the corresponding numerically-obtained recovery. However, in terms of the overall performance, the difference between the cumulative oil production between the laboratory experiments and numerical model is only four percent. This is due to the rate alteration required under laboratory conditions. The sensitivity assessment indicated that the relative permeability curvature interpolation and the polymer concentration are the most dominant independent parameters impacting the final oil recovery (in the homogeneous model). For a 2D model, the propagation of the polymer front remains uniform compared to the more pointed water fronts in the WF. A significant reduction in the total mobility from 10 mD/cp to 5.2 mD/cp confirms the sweep efficiency enhancement. The history-matching results suggest that the saturation endpoints have been altered during PF in comparison to the WF. This behavior was reported as inconclusive for several polymer-flooding options. The meta-model’s sensitivity assessment highlights a strong relationship between the polymer concentration and the final oil recovery for the okra-based polymer flooding.

As part of pre-CO2 storage development plan assessment, geomechanical risk analysis is conducted on the field of interest to determine maximum injection pressure as well as the associated geomechanical risks during injection and long-term storage. In the analysis, first fully calibrated and validated 1D geomechanical models are developed from the available log data of the wells in the field of interest. Using the rock mechanical properties as input, caprock integrity analysis is conducted and maximum injection pressure is determined to ensure the caprock does not experience tensile or shear failure during CO2 injection. If there are faults penetrating the caprock and reservoir of interest, fault stability analysis is conducted on the faults to ensure the faults remain stable. Finally, reservoir expansion and seabed uplift analyses are conducted to determine the maximum surface uplift. The methodology is illustrated with examples from a CO2 storage field candidate located in offshore Peninsular Malaysia. Rock mechanical properties and stress data were extracted from the 1D geomechanical models at formation of interest for caprock integrity analysis to determine maximum injection pressure and for fault stability analysis to maintain stability of the faults. Seabed uplift was calculated for use in surface facility integrity evaluation.

Shale instability in shale gas drilling can cause significant disruptions, including wellbore failure and operational inefficiencies, making it a key issue that demands innovative solutions for smoother and more cost-effective drilling processes. Various additives including Potassium Chloride (KCl), 1-Ethyl-3-methylimidazolium chloride. ([EMIM]Cl), SiO2 nanoparticles, amine terminated polyetheramine (ATPE), Okra mucilage, Choline Chloride:Urea Deep Eutectic Solvent (DES), and Citric acid:Glycerine Natural Deep Eutectic Solvent (CA NADES) and their combinations were subjected to rigorous examination to delineate their impact on shale stability and drilling fluid properties. Overall CA NADES improved YP/PV and resulted into 42.8%, 40.32% and 76.1% decline in mudcake thickness, filtrate volume and linear swelling respectively with 51.68% improved shale recovery. Moreover, SET B (0.5% KCl + 0.5% ATPE), and SET G(0.5% KCl + 0.5% [EMIM]Cl) emerged as particularly promising candidates for ameliorating shale swelling and enhancing drilling fluid properties. In conclusion, this study underscores the pivotal role of additives in assuaging shale instability and optimizing drilling fluid formulations. The elucidation of additive efficacy in mitigating shale instability and optimizing drilling fluid performance holds profound implications for real-world drilling operations, promising enhanced efficiency, and reliability in the extraction of vital subsurface resources.

Geopolymer cement, a sustainable alternative to conventional Portland cement, offers significant environmental benefits through reduced CO2 emissions and the utilization of industrial by-products, such as fly ash. However, a notable challenge with geopolymer cement is its tendency for strength retrogression under high curing temperatures. Thus, this study investigates the effects of incorporating silica flour into fly ash-based geopolymer cement, examining its impact on key properties including rheology, thickening time, fluid loss, and compressive strength. Ultrasonic cement analyzer (UCA) equipment was utilized to determine the compressive strength of the geopolymer cement at 3300 psi and 120 ℃ (BHST), which is the Field X’s downhole condition Findings reveal that the incorporation of 35% silica flour by weight of cement (BWOC) significantly mitigates strength degradation, enhancing both the rheological properties and thickening time while effectively reduce fluid loss. The results show that 35% silica flour by weight of cement (BWOC) mitigates the strength degradation of silica-stabilized geopolymer cement (SSGC). These enhancements highlight the pivotal role of silica flour in refining the geopolymer matrix, optimizing the cement's mechanical and durability aspects. The research accentuates silica flour’s potential in fostering the development of more robust, efficient, and eco-friendly fly ash-based geopolymer cement.

Saline aquifers offer promising potential as storage sites for CO2. However, the storage of CO2 in deep saline aquifers, particularly those containing brine with exceptionally high salinity, presents distinct challenges in experimental preparation. To address this issue, molecular dynamic simulation emerges as a vital tool. Conventionally, studies in this domain often employ the use of a single salt, such as NaCl, to replicate the brine of saline aquifers. However, this approach may not fully capture the intricacies of actual brine behavior. Hence, the objective of this study is to construct a comprehensive brine model comprising both divalent and monovalent salts, with their proportions accurately reflecting real aquifer conditions. By mimicking the properties and behavior of actual brine found in saline aquifers, the goal is to ensure the accuracy and applicability of the model to real-world conditions. Parameters such as temperature, pressure, and concentrations are carefully tuned to mimic the conditions found in high-salinity aquifers. The resulting brine model is validated against density and viscosity data to ensure accuracy and relevance. This work not only contributes to the understanding of brine solutions in saline aquifers but also provides a realistic model for future researchers. Moreover, the simulated brine model will bridge the gap between theoretical modeling and experimental challenges.

Improved oil recovery is one area where nanotechnology is having a profound impact on the oil business. This is mainly due to the creation of hybrid nanofluids, complex fluids, and mixtures of functional nanoparticles and fluids. However, there is a need to characterize these hybrid materials that would be used in EOR at reservoir condition. This paper proposes the development of a new hybrid nanoparticle (Graphene/SiO2) that is stabilized using a natural surfactant (Gum Arabic and/or SCMC) via a simple, direct, and eco-friendly process (LPE-Liquid Phase Exfoliation) to improve oil recovery. The optimized functionalized nanoparticle would serve as a basis for further study on using this nanomaterial in EOR. The optimized stability for GA surfactant is −37.13 mV (1:9 ratio), and for SCMC is −67.57 mV (2:8 ratio). The optimized particle size for GA is less than 210 nm which are found in a wide range of regions with most of the concentration around 0.5 wt%. While the optimized size for SCMC is less than 300 nm with concentration lower than 0.5 wt% but greater than 0.1 wt%.

The soil nutrients in a certain agricultural region are chosen to optimize the possibility of high crop yields. Local farmers have raised concerns about the prevalence of common heavy metals, including lead, mercury, arsenic, and cadmium, in soil sediments. These concerns are particularly focused on areas near polluted bodies of water. These are laboratory techniques used to reduce the amount of heavy metals in soil and to create the right amount of nutrients by applying appropriate fertilizers. The issues can be resolved by doing a comprehensive soil analysis. The main goal of this project is to create a versatile Internet of Things (IoT) device that uses Raspberry Pi and NIR spectroscopy for soil analysis. This device aims to reduce the need for farmers to perform time-consuming laboratory soil testing and to offer cost-effective soil analysis. The device was operated using an Arduino MEGA 2560 connected to a TCD1304AP. By setting up the Internet of Things (IoT) on Raspberry Pi using Django and pgAdmin, any user connected to the specified WiFi network can access a webpage that shows different outcomes. The results encompass data regarding the composition of the soil, its pH level, suggested crops, and potential remedies and treatments for the soil. The data analysis demonstrated a precision of 95% in determining the quantities of macronutrients in the soil. The proposed system exhibited a 25.56% margin of error and a 28.57% disparity in pollutant concentrations.

The primary aim of this study was to examine the impact of temperature and fluid salinity on carbon dioxide (CO2) injectivity. The injectivity experiment was conducted using a core flooding unit with Berea sandstone representing the porous media and sodium chloride (NaCl) as the formation fluid. To assess the relative effect of temperature and salinity on CO2 injectivity impairment, the temperature of CO2 injection was varied from 27 to 100 °C, and the formation fluid salinity ranged from 6000 ppm to 100,000 ppm. CO2 injectivity alteration was determined by calculating the relative injectivity change (RIC) between the initial and final permeability. Results indicated that CO2 injection below the supercritical temperature had no effect on the RIC. However, CO2 injectivity showed a significant reduction of approximately 56.52% at 40 °C, with the reduction in impairment gradually decreasing to 32.2% at 100 °C. The findings also demonstrated that increased salinity led to more salt precipitation, which could narrow pore channels, ultimately reducing porosity and permeability, thereby affecting injectivity. This study is among the first experimental efforts to validate the relative effects of temperature and salinity on CO2 injectivity impairment. The insights gained from this research could enhance the understanding of CO2 injectivity impairment in deep saline reservoirs.

Implementing remote operation for Platform A and B is a strategic move towards a more efficient and fit-for-purpose approach in managing Operations and Maintenance (O&M). With high operating and maintenance costs, operating offshore platforms have become very challenging. Platform A currently equipped with pneumatic instrumentation/control and shutdown systems without remote capabilities, and Platform B having electronic systems with some remote functionalities, there is a clear opportunity to enhance operational efficiency and safety. The transition to remote operations can provide benefits such as reduced physical presence on the platform to lower operational costs and risks associated with offshore work, enhanced monitoring and control capabilities that can lead to better proactive maintenance and quicker response to issues and improved data collection and analysis for better decision-making and optimization of O&M processes. Considering the geographical location of the platforms, remote operation can also help in overcoming the challenges posed by distance from the mainland. It’s important to ensure that necessary infrastructure, such as reliable communication links and advanced control systems, are in place to support this transition. With the concept of remote operations, we intend to implement this on both platforms so that we have a more fit-for-purpose approach towards operation and maintenance.

The use of remote sensing satellite data has reduced the need for physical outcrop visits for data gathering and geological map generation. Since, snow covered and plants pose a difficulty in polar and tropical locations around the world, then digital image processing techniques must be used to reveal the lithology beneath those superficial layers. The surface geology in the Kuantan area of “Pahang State” in peninsular Malaysia was analyzed using band ratios and subjected to principle component analysis (PCA) on landsat-8 multispectral satellite data. A mineral based classification system was adopted using mineral indices to highlight minerals such as iron oxide, silica, and clay that are masked by the vegetation cover. Band ratios were used for mineral indices like (BR 6/7 for clay minerals), (BR 4/5 for vegetation suppression), (BR 4/2 for ferric iron oxide), and (BR 6/5 for ferrous iron oxide) and were subsequently subjected to the PCA image enhancement technique. The results generated were compared with field collected data for ground truth assessment.

Drilling in unconventional shale formations poses significant technical and operational challenges. The effectiveness of the drilling operations in these challenging formations depends on the appropriate selection of the drilling fluid. Oil-based drilling fluids provide excellent performance when drilling these formations. However, the adverse environmental effects of oil-based drilling fluids have restricted its usage globally owing to strict environmental restrictions. Biodiesel-based drilling fluids have become a substitute for traditional oil-based drilling fluids and are favoured for their strong capacity to break down naturally and their little harmful effects. This research work produces biodiesel from the non-edible Calophyllum inophyllum oil via a process of two-step esterification and transesterification. An extensive experimental study was conducted to analyse the rheological behaviour, filtration characteristics, and emulsion stability of the formulated drilling fluid under both normal and reservoir conditions. In addition, shale inhibition tests were also performed to examine the effect of the formulated drilling fluid on the swelling properties of the clay-rich shale formation under high pressure and high temperature conditions, with an exposure duration of 24 h. The drilling fluid’s formulation improves rheological properties, increases emulsion stability and lowers filtration loss following API standards. The drilling fluid demonstrated outstanding performance with a maximum clay swelling of 5.4% at high pressure and high temperature conditions. The outcomes demonstrate that the formulated biodiesel-based drilling-fluid has the potential to substitute oil-based drilling fluid as an appropriate technical and environmental alternative for drilling unconventional shale formations.

The steam injection is a perfect alternative for increasing heavy oil production. The conventional monitoring obtains a temperature profile by running a wireline log across the desired interval, and then using a conventional correlation to forecast in the future. The conventional technique is called the heat mining tool (HMT). A sector model was developed to determine a correlation between the alteration temperature in the reservoir and steam flood parameters such as steam quality, and stream rate. The simulation model encompasses an inverted 7-spot pattern. The numerical model is synchronized with the actual temperature data in the field by history matching technique. The model’s correlation is to forecast the coldness trend in the pattern. The first sand layering is the main injection target with an injection period of 10 years from 2009 to 2019. The model is utilized to observe the steam distribution, steam flow path, and the rate of temperature decline. In addition, the model’s correlation is compared to the HMT to analyze the dissimilarity. From the management perspective, it is an opportunity to have an alternative method for reservoir management. The model has a close boundary effect. Hence the pseudo wells inserted techniques are used to reach the history matching synchronization. The pseudo well-A delivers the oil influx into the pattern between wells #2 and #4. Meanwhile pseudo well-B conveys steam to region between wells #3 and #5. The simulation result shows that the steam injection rate affects significantly into the reservoir temperature alteration compared to the steam quality.

Nanofluid-enhanced oil recovery (EOR) technology leverages nanoparticles dispersed in fluids to interact selectively with reservoir rocks or crude oil, significantly boosting oil recovery rates. Despite its advantages over traditional methods like binary and ternary flooding, practical implementation faces substantial challenges. Economic feasibility, nanoparticle stability, and suitability remain significant barriers to widespread adoption in the oil sector. Addressing these concerns involves exploring simpler, more readily available materials and basic modification techniques to meet large-scale on-site application demands. This study synthesized MnO2-CoO nanoparticles to investigate phase transition effects on oil recovery factors. Field-emission scanning electron microscopy (FE-SEM) and energy-dispersive X-ray spectroscopy (EDX) characterized nanoparticle morphology and elemental composition. Fourier-transform infrared spectroscopy (FTIR) identified functional groups, while X-ray photoelectron spectroscopy (XPS) confirmed mineral composition and electronic states. Electromagnetic field integration assessed fluid flow using absolute and relative permeability. Results indicate that a 0.5% nanoparticle concentration yielded the highest recovery factor in interfacial tension (IFT) and core-flooding experiments, highlighting MnO2 nanoparticles’ effectiveness in enhancing oil recovery. The permeability tensor of MnO2 nanoparticles was measured under magnetic fields, whereas CoO exhibited antiferromagnetism due to the antiparallel alignment of cobalt ions, neutralizing net magnetic moments.

The success of geological carbon storage (GCS) projects is dependent upon the sealing integrity offered by the caprock. The adsorption of carbon dioxide (CO2) onto shale, the most common type of shale, is a key consideration for evaluating its integrity. A review of literature indicates a lack of studies regarding CO2 adsorption onto shale, particularly in the context of deep saline aquifers. Furthermore, investigations on the CO2 solubility in brine, leading to acidic conditions, on the CO2 adsorption onto shale are limited. This study employed the volumetric approach to investigate the adsorption of CO2 onto Eagle Ford and Terengganu shales under different storage conditions, particularly acidic conditions. The results indicated that CO2 adsorption capabilities of the Eagle Ford and Terengganu shales varied between 0.183 to 2.63 mmol/g, and 0.103 to 2.08 mmol/g, respectively, when the pressure changed from 250 to 1100 psia at 333 K in non-acidic environment. Moreover, CO2 exhibits a stronger preference for both Eagle Ford and Terengganu shales under acidic conditions, suggesting that enhanced CO2 adsorption in acidic environments is independent of shale mineralogy and aquifer conditions. This study establishes that the performance of shales in retaining CO2 is reduced in the presence of acidic conditions, higher pressure, and elevated brine salinity.

Distributed acoustic sensing (DAS) is a state-of- the-art technology that is gaining popularity for seismic data acquisition that records seismic signals using fiber optics instead of geophones as a sensing tool. In most published studies, DAS with vertical seismic profiling (VSP) technique is generally used for subsurface imaging. In investigating new areas or regions without well-established access, surface DAS becomes a valuable and optimal option. Surface DAS does not require boreholes or wells at all because it uses fiber optic cables that are placed on the surface as a sensor array to capture the seismic wave that traveled back to the surface from wave propagation into the subsurface. In this paper, we present a MATLAB-based simulation for studying the impact of different gauge length on simulated surface Distributed Acoustic Sensing (DAS) data in terms of signal quality and subsurface imaging resolution. In summary, whereas longer gauge lengths in surface DAS simulations can lead to better directivity and SNR signal quality, their effects on subsurface imaging may not always be significant. In order to effectively balance signal quality and imaging resolution, the ideal gauge length should be chosen after giving careful consideration to variables such as source frequency and subsurface velocity.

Among the acquisition technologies in the subsurface exploration that is expanding the fastest is the use of fiber optic cables as distributed sensors to monitor the subsurface. The technology has advanced into Distributed Acoustic Sensing (DAS) system for seismic imaging in scope of exploration and production. DAS has been used to track the CO2 injection activities at the Aquistore CCS sequestration project and has been shown to be a useful instrument for getting VSP. The technology may be used for a variety of purposes, which makes it an affordable and adaptable monitoring solution. Nonetheless, certain features of DAS, like the measurement’s single component character, act as obstacles to the technology’s adoption. DAS technology generally measures strain rate data and detect seismic wave variation in recorded signal data on cable. Because DAS measuring strain rate rather than particle velocity, therefore the signals it records cannot be easily compared to those from geophone. Understanding the seismic source, subsurface reflection response, cable geometry, and instrument impacts is therefore essential to comprehending the DAS response. In this paper we present our simulation modelling approach for DAS response over CO2 storage over Smeaheia field located on offshore Norway, refers to cable geometry. The Smeaheia has been observed as a CO2 storage project due to its complex subsurface features that made it a potential in injecting CO2. The simulation workflow exploits synthetic subsurface models from Smeaheia field seismic section of different complexity which in the end are representative of Smeaheia geology features. Using open-source Matlab Gui software ‘ExploreDAS’, we were able to observe and compare these modelling approaches in relation to DAS strain rate response. With varying input parameters complexity such as gauge length and the seismic wave, we manage to image and observe DAS response for synthetic cable geometry. This cable geometry represents both surface and VSP (wellbore) deployments, which replicate fiber placed injection well and deployed on the sea floor.

Gaining a thorough understanding of the subsurface structure in volcanic rock areas is crucial to improving the assessment of risk and uncertainty in oil and gas development. However, when large areas need to be examined, it can get expensive to map the main features of the geological layers. The utilization of passive seismic as an alternative method in exploration activities could serve as a potential solution to the high cost of measurements. Over a two-month period, seventy stations were recorded in a configuration of two intersecting lines, with thirty-five stations on each line. This work presents a technique that employs the horizontal-to-vertical spectral ratio (H/V) of single-station noise recording analysis to image subsurface structure by identifying impedance contrasts. The difference between the initial H/V spectrum and the smooth H/V spectrum at each measurement point is used to calculate the S-Wave impedance contrast. Lateral interpolation is done on each line to create a cross section. The processing results obtained from the two lines of this method suggest the existence of a thrust fault structure, which could be responsible for the observed surface structure in this region which could be related to the petroleum system that works in this region.

Economic growth, increasing global energy demand, and the depletion of known reserves are driving the oil and gas industry to explore deeper reservoirs. These deeper formations, particularly in brownfields, present unique challenges, with recovery factors often 10–30% lower than expected. Exploration Well A in the Z reservoir confirmed the presence of hydrocarbons but revealed significant challenges due to the reservoir’s low porosity and permeability. Limited Special Core Analysis (SCAL) data, caused by high costs, further complicates reservoir characterization and decision-making. Machine learning (ML), a branch of artificial intelligence, offers a groundbreaking approach to address these challenges. ML algorithms learn from data and adapt without explicit programming, making them highly effective in improving reservoir evaluation. By analyzing raw log data, ML models reduce reliance on fixed constants in traditional equations, leading to more accurate and consistent predictions. This study deployed ML techniques to predict porosity, permeability, and moveable fluid volumes in selected wells across the Sarawak Basin, Sabah Basin, and Malay Basin. The results demonstrated that ML predictions aligned well with conventional interpretations, with deviations consistently within acceptable thresholds. The technology also highlighted potential producible zones, including thinly laminated formations that conventional methods often overlook. The study emphasized the critical role of diverse, high-quality training datasets in addressing geological variations and borehole complexities. By integrating ML into reservoir workflows, the industry can enhance reservoir evaluation, reduce uncertainties in volumetric estimates, and support more reliable decision-making. This approach not only optimizes recovery strategies but also provides a path forward in managing the complexities of deeper reservoirs more effectively.

Ambient noise tomography (ANT) has become a powerful technique for imaging the Earth’s subsurface by utilizing the seismic noise recorded by seismometers worldwide. However, to successfully implement ANT, certain requirements must be met, such as sufficient data length and dense sensor network with a high-performance computer to do the calculation. In this study, a robust and computationally efficient ANT algorithm is presented. It is designed to determine subsurface velocity models with minimal computational resources. To validate the effectiveness of the algorithm, synthetic models that incorporate unique source characteristics, serving as a controlled environment for evaluating the inversion process are developed. These synthetic tests demonstrate the algorithm’s ability to manage complex source configurations and produce accurate and detailed velocity models. The ANT algorithm is applied to real-world seismic data from two geologically distinct regions: South California and the Appalachian Mountains. The resulting velocity models provide valuable insights into the subsurface structure and properties of these areas, revealing intricate geological features and anomalies that have important implications for understanding the region’s tectonic history and present-day dynamics. The successful application of the ANT algorithm to both synthetic and real-world data demonstrates its potential for a wide range of geophysical applications, including geothermal exploration, oil and gas prospecting, and geohazard assessment. This study represents a significant advancement in the field of subsurface imaging, offering a robust and efficient tool for unraveling the complexities of the Earth’s interior.

As the world grapples with the urgent need to transition from fossil fuels to renewable energy sources, geothermal energy emerges as a promising solution, particularly in regions with untapped potential. This study delves into the geothermal energy prospects of Central Luconia’s depleted gas fields, specifically focusing on the F06, F23, and M01 fields. By examining their thermo-hydraulic properties, we uncover strategic opportunities for harnessing geothermal energy in this region. Our findings reveal that while the F06 field stands out for its economic viability in direct-use applications, the F23 field presents moderate thermal conditions with challenges in fluid mobility. The M01 field, despite its high thermal potential, faces significant permeability issues. This research not only highlights the geothermal potential of Central Luconia but also paves the way for innovative approaches to sustainable energy development.

In geoscience, particularly in fields like sedimentology and hydrogeology, the presence or absence of sand can be crucial for various analyses and interpretations. Although there are multiple researches that worked on distinguishing sand from non-sand, an essential gap is there to estimate the probability of sand and the associated uncertainty of the prediction. Sand probability measurements provide valuable information about the distribution, geometry, and connectivity of sand bodies within the reservoir. This information is critical for reservoir modeling, volumetric calculations, and production forecasting. The estimation of sand probability is achievable by introducing randomness using the non-deterministic machine learning models. The novel goal of the current study is to estimate the probability of sand facies as well as measuring the uncertainty in the prediction while perpetuating the high accuracy by introducing non-deterministic XGB model. The proposed approach is able to achieve an F1 Score of 89% in classifying sand. Additionally, the prediction uncertainty and sand probability (P10, P50 and P90) are also calculated in unseen or blind well by using the Seismic attributes.

The precise determination of geomechanical parameters has been seen as vital for alleviating the potential problem of the wellbore's instability during drilling operations. In fact, the migration of fine particles can destroy the natural permeability of sediment, a process that can severely impact wellbore integrity. A careful estimation of these components is essential for drilling optimization and the well completion process. Minimum horizontal stress, which one of the three principal stresses, is crucial for assessing both hydraulic fracturing and wellbore stability. This study present a data-driven technique for predicting the minimum horizontal stress in five wells located in the Eocene Agbada Formation of the Niger Delta. Six machine-learning models were applied to the traditional logging data to predict the minimum horizontal stress. The comparison analysis of the applied algorithms shows that the gradient-boosting outperformed the other types. They give the best performance with the highest R2 of 0.92, lowest MAE of 279.66 on the training set, and high R2 of 0.93, and lowest MAE of 273.53 on the holdout set. By doing a blind test on two wells, we were able to confirm the effectiveness of the suggested method. The model was affirmed to be generalized with low generalization errors on the unknown data.

This study presents a numerical fatigue analysis of three riser configurations, Shaped Steel Lazy Wave Riser (SSLWR), Steel Lazy Wave Riser (SLWR), and Steel Catenary Riser (SCR), for ultra deepwater applications. Also, the effectiveness of SSLWR compared to SCR and SLWR under different environmental loading conditions was evaluated. The analysis was performed using OrcaFlex, a 3D nonlinear finite element software to determine and compare the fatigue life of the three riser configurations under different environmental conditions. The performance of these risers at the hang-off point (HOP) and touchdown zone (TDZ) across five depth cases ranging from 914 to 3000 m was analyzed. SSLWR consistently outperforms SCR and SLWR at the HOP, with fatigue life of up to 796.98 years at 3000 m, compared to SLWR’s 46.67 years and SCR’s 0.78 years. However, at the TDZ, SSLWR showed significant variability, with a dramatic decrease in fatigue life when horizontal distances were increased.

The tension and stress experienced by Steel Catenary Risers (SCRs) and Steel Lazy Wave Risers (SLWRs) increase with the length of the risers. In this work, the tension and stresses of a newly proposed Shaped Steel Lazy Wave Riser (SSLWR) is compared to that of a SLWR and Steel Catenary Riser (SCR). Simulations were carried out using OrcaFlex to determine effective tension and von Mises stress. Subsequently, a comparative study of SCR, SLWR and SSLWR at different water depth was performed. It was found that SSLWR reduced the effective tension and von Mises stress significantly by 86.28% and 99.86% respectively compared to SCR at a depth of 3000 m. While SSLWR can be used, the use of SCR and SLWR would be preferable for compliance with standards. There is scope for improving the riser performance as the depth increases to ultra-deepwater exploration.

Injecting CO2 into geological formations is an emerging method for enhancing geo-storage security. The effectiveness of such projects heavily relies on understanding rock wettability and interfacial interactions, which influence capillary pressure and, consequently, caprock integrity (structural trapping) and residual trapping processes. This review focuses on the CO2–brine–rock wettability and interfacial tension under varying temperature, pressure, and salinity conditions, using both experimental techniques and Molecular Dynamics (MD) simulations. Comparison with experimental data from the literature reveals correlations between contact angles and parameters such as pressure and salinity, with a general trend of decreasing contact angles with increasing temperature. However, inconsistencies in temperature-dependent behavior across datasets were observed. MD simulations provide a promising route for reconciling discrepancies between experimental measurements, particularly regarding contact angles, and enhancing fluid-rock interaction insights.

Sand production is one of the major problems associated with oil and gas production. Stand-alone sand screens (SAS) are widely used for sand control due to their simplicity and low cost. SAS can face failures, mainly due to erosion. Existing erosion prediction correlations have limitations due to their reliance on middle particle size. Furthermore, screen erosion modeling studies that consider viscous fluids are limited. The aim of this study is to improve the erosion modeling of standalone sand screens under high sand concentration, considering particle–particle interaction and carrying fluid properties. In this study, a CFD-based modeling approach is used, applying the Eulerian–Lagrangian approach of the dense discrete phase model (DDPM) to track particles. Four sand types with different particle size distributions (PSD) are studied. A new erosion equation with good accuracy was developed to improve the prediction of SAS erosion. The proposed equation demonstrates its novelty, compared with existing correlations, as it incorporates the carrying fluid viscosity parameter, along with parameters associated with sand specifications, such as mean particle diameter, SC sorting coefficient, fine percentage, and particle concentration. The findings of the study are of great significance for sand screen erosion studies. The results show the importance of fluid type consideration for erosion estimation, especially if SAS is tested using air in laboratories. The proposed equation can be used if the description of the produced sand and the carrying fluid are known.

The Miqrat Formation is a tight gas reservoir in the interior Oman Salt Basin, situated in the Huqf area of central Oman. The formation was initially thought to be deposits of aeolian dunes, wet and dry sabkha, and playa lakes. However, recent reevaluation of the formation reveals new geological insights. This research utilizes outcrop-based facies analysis to interpret the paleodepositional environment. Based on the lithology, sedimentary structures, textures, and trace fossils, the formation can be classified into three informal members. The lower member is composed of reddish-brown mudstones and sandy siltstones with coarsening upward cycles. The fine-grained sandstone exhibits wavy ripples and low-angle cross-laminations, and large-scale desiccation cracks. The upper member is poorly exposed, showing lithological similarity to the lower member. The middle member is Pinkish–brown, fine to medium-grained planar and trough cross-bedded sandstones and wavy ripples. At the top of this member, the planar cross bedded sandstone exhibits Thalassinoides trace fossils. The sedimentary facies and trace fossils suggest the formation was deposited in a marginal marine environment, with tidally influenced (supratidal to intertidal) conditions in the lower and upper members and a subtidal setting in the middle member.

This technical paper shows the proof from the inspection results of internal surface degradation or defects of ferromagnetic steel pipe sample by Metal Magnetic Memory (MMM) technology. The inspection result using the technology validated by the sample itself which proven for the type of defects and location. In this case, the defect validated is a known corrosion at internal surface of steel pipe sample. The output (magnetogram) of the inspection result shows that the scanning results from Metal Magnetic Memory (MMM) technology on the known degradation sample of corrosion and pitting have distinctively shown distinctive signature of magnetogram when compared with defect-free material. MMM’s magnetogram of Self Magnetic Leakage Field (SMLF) on known defect material shown a burst signal at the screening location, giving indication of its capability or prove of successful detection defects on ferro-magnetic materials’ internal surface, This proof test results indicate that MMM technology can provide information for monitoring and detecting degradation of ferromagnetic materials, defects screening to monitor internal pipe corrosion and cracks detection by only refer to the signal output magnetogram of Self-Magnetic Leakage Field (SMLF). Metal Magnetic Memory (MMM) technology system is based on the concept of self-magnetic leakage field (SMLF) measurement at the surface. The technology is passive, non-intrusive of advanced NDT and it does not require any induced energy applied to the system to detect material defects or material degradation. Its application for detecting material defects or degradation of material-corrosion is very robust, fast, no need for prior cleaning or preparation process. In addition, the inspection or screening can apply to insulation coating pipe and economical where its inspection can cover wide inspection area with arrangement number of sensors. MMM technology has now gained attention as condition monitoring and tools for periodic inspection schedule, and base-line data not required.

Two wells were drilled in Offshore Sarawak, namely Well S2, an appraisal well positioned downdip of Well S1, a discovery well, with objectives to enhance the understanding of the resource potential to fully evaluate the reservoir quality and hydrocarbon potential of Cycle II gas-bearing sands. The well evaluation program in both Well S1 and S2 incorporated Logging While Drilling (LWD) and wireline logging. Rotary sidewall cores were obtained in Well S2 and were analysed for comprehensive lithology and rock properties, while Modular Formation Dynamics Tester (MDT) was used for extensive formation testing and fluid sampling to determine the fluid types and contacts. Extended well testing post-completion was performed for selected gas-bearing reservoirs within Cycle II to gather fluid properties for reservoir characterization and production potential assessment. To meet the objective of the study in this field, a comprehensive petrophysical evaluation was conducted by integrating all data obtained in these two wells. Reservoir properties such as lithology, volume of shale, porosity, permeability, and water saturation are the basic inputs required for building static geological model and dynamic reservoir model. This case study presents a comprehensive petrophysical evaluation of the two wells, to enhance our understanding of the Cycle II reservoir sands.

Carbonate reservoirs are still attracting oil and gas investors given the fact that the largest producing field in Ghawar Field is from carbonate reservoir. However, because of their heterogeneity and complex depositional settings, carbonate body identification poses special challenges. Conventional techniques for detecting carbonate bodies in seismic frequently depend on labor-intensive and subjective manual interpretation which are time consuming and prone to human error. Over the years, the usage of Artificial Intelligence in the workflow of geoscience has attracted the attention of researchers. Image analysis via machine learning or deep learning has promising results for application to exploration and production technologies. The demands for the automation of carbonate body detection in seismic to shorten the delivery time of work have been growing. In this study, the authors propose a hybrid image analysis technique based on deep neural network for carbonate detection in seismic which is an eye-catching process by most of the exploration geophysicists using the Yolov5 model. By employing convolutional neural network (CNNs) algorithm, the proposed method can analyze vast amounts of seismic data with high accuracy and efficiency. In addition, this image analysis is based on pattern detection that combines geological knowledge with machine learning techniques to overcome the challenges in detecting carbonates especially in complex depositional settings. The study concludes that integrating automated detection into seismic interpretation can revolutionize the field, enhancing and reliability of subsurface geological interpretations. This advancement in automation can significantly reduce the time and cost associated with seismic data analysis, providing a more consistent and objective interpretation of carbonate Future research will focus on refining the model by adding segmentation, incorporating more diverse and complex datasets and integration of seismic signals to the detection.

Analog Sandbox Modeling (ASM) is a physical modeling method used to illustrate the formation of geological features such as faults and folds in sedimentary basins that could host petroleum accumulation and storage systems. These basins are commonly deformed. This research aims to investigate the characteristics and distribution of geological structures formed in an extensional system above the basal detachment with a planar footwall. Homogeneous isotropic material (i.e., 50–40 mesh-sized quartz sand or 0.3–0.4 mm) was utilized, exhibiting linear properties with a Navier-Coulomb behavior curve and a friction angle (Ø) 31˚. Five trials were simulated, each trial initiated from 25% and ended with a 100% extension. Experiments 1–5 resulted in 14–15 faults at 100% extension, with an increasing number of faults observed at higher extension rates. Although synthetic normal faults are dominant, antithetic faults were consistently formed first. The faults were initially planar, and they became listric as extension continued. The relationship between fault dip angles and extension magnitude showed a trend where the synthetic normal faults experienced decreasing dip angles, while antithetic faults exhibited increasing dip angles, with extension. Insights from this sandbox modeling could be used as a consideration in understanding sedimentary basins with extensional tectonic regimes.