bluuebunny/crossref_metadata_2025 huggingface dataset:
"Capitalizing on the Wealth Buried Deep Within Living Matter," or Politics and Patents
Authors: Horsley Jane
Published: February 2025

The intellectual property rights have become the mechanism of choice for "capitalizing on the wealth buried deep within living matter" by providing patents and copyrights in the products and processes of biotechnology. The most infamous of these developments was the U.S. Patent for the "Transgenic Mouse' - a mouse genetically engineered for a particular oncogene, "enabling it to get cancer on demand." [...]
Spirit Rising
Authors: Berman Tzeporah
Published: February 2025

They tell me there's no connections ...But I hear them talk about the rape of the landand I feel like I've been violated.
Toma de decisiones en la gestión sostenible del transporte: revisión bibliométrica de la literatura
Authors: Orobio Juan C. , Guzmán Daniel E. , Murillo Hoyos Jackeline , Jaramillo Molina Ciro
Published: February 2025

Introducción: Tradicionalmente, en la gestión del transporte, las decisiones se han tomado utilizando el análisis costo-beneficio para comparar diferentes alternativas. Sin embargo, este método no fue diseñado para contemplar las dimensiones de sostenibilidad. En este contexto, en los últimos años se han utilizado índices de sostenibilidad y el análisis multicriterio como alternativas.Objetivo: Este artículo tiene como objetivo identificar las tendencias globales en los procesos de toma de decisiones en la gestión sostenible del transporte mediante una revisión bibliométrica de la producción científica.Metodología: El artículo se desarrolló siguiendo cinco pasos metodológicos: i) búsqueda bibliográfica, ii) selección para inclusión, iii) extracción de datos, iv) selección de fuentes de datos y v) análisis de datos.Resultados: Los resultados evidencian una dinámica significativa en la búsqueda de herramientas adecuadas para implementar los conceptos de desarrollo sostenible en la toma de decisiones, destacando especialmente la necesidad de considerar el contexto específico de las ciudades, la distribución espacial de los efectos y la participación de los actores involucrados.Conclusiones: El estudio subraya la necesidad de integrar las dimensiones de sostenibilidad en los marcos de toma de decisiones en la gestión del transporte, con un enfoque en factores contextuales específicos y en la consideración de los actores involucrados para implementar de manera efectiva prácticas sostenibles.
Formation-Dependent Tectonic Lateral Strain Calibration for CCUS Operations Applying Microfrac Tests
Authors: Ighodalo E. , Berkane M. , Franquet J. , Chakrabarti P.
Published: February 2025

Obtaining a consistent workflow to determine the tectonic lateral stress boundary conditions is a critical subsurface input for building reliable 3D geomechanical models. These models are used to predict cap-rock integrity and seal assessment in CO2 injection projects. This paper compares multiple workflows and tectonic calibration techniques using constant and variable lateral strain values across several formations from Late-Cretaceous to Middle-Triassic.

Tectonic lateral strains are obtained from multiple microfrac tests conducted across different bore-hole sections of a single well drilled. The rock mechanical properties are derived from density, neutron porosity, and acoustic cross-dipole logging data acquired before the downhole formation testing run. Formation breakdown, fracture reopening, and closure pressures are measured on each straddle packer microfrac station across both clastic and carbonate formations. The minimum and maximum horizontal strains are fine-tuned to back-calculate the magnitude of the minimum and horizontal stress until the predicted fracture closure and formation breakdown match the result of the multiple microfrac tests.

Cross-plot correlation coefficients are calculated between log-predicted and microfrac-obtained fracture closure and formation breakdown to compare different methods of tectonic strain derivation. One method uses a constant value for the minimum and maximum strains while a second method uses variable strain values with depth allowing different tectonic lateral conditions on each lithologic formation. Model sensitivity analysis on the methodology of assuming constant poroelastic Biot coefficient values versus estimating Biot from formation porosity logs is also reviewed. The average horizontal strain values obtained have a satisfactory match with the microfrac subsurface measurements. The variable strain method obtains better correlation coefficients for both fracture closure and formation breakdown producing a more accurate match with the multiple in-situ measurements.

This study reveals a very useful methodology to derive accurate and formation-dependent lateral strain tectonic boundary conditions for calibrating geomechanical models across the field in subsurface CO2 injection projects. Accurate prediction of formation breakdown pressure is fundamentally required to constrain the CO2 injection rate and downhole pressure for safe and contained carbon sequestration and storage.
An Integrated Approach to Enhancing Reservoir Property Prediction in a Clastic Reservoir: A Case Study from Acacus Formation, Ghadames Basin, NW Libya
Authors: Alshaafi H. , Elakuri A. , Albrouki M. , Arfaad F.
Published: February 2025

The ultimate goal of this study is to investigate the reservoir architecture in the northern part of Ghadames Basin, Libya, which is known for its highly complex reservoir with multiple layers, using precise 3D modeling with low uncertainties. To overcome the associated facies changes and reservoir complexity of the Acacus formation, the reservoir architecture, petrophysical parameters, structural characteristics, and facies distribution must be understood and recognized. A comprehensive methodology was applied in this study which incorporated facies interpretation, sequence stratigraphy, and machine learning, Embedded Model Estimator (EMBER) to enhance the accuracy of 3D porosity and permeability modeling. Facies interpretation involved core analysis, log signatures, and dip meter data to determine paleocurrents. Seismic data was also utilized, with the root mean square (RMS) attribute proving to be particularly effective in defining different depositional trends. Truncated Gaussian simulation (TGS) was used for facies modeling, enabling population and shoreline delineation. The facies and rock types were populated using the hierarchical modeling approach, using the Sequential Indicator Simulation (SIS) algorithm. The results of this study verified that the lower part of the Lower Acacus can be interpreted as progradational regressive fluvial-deltaic bodies, which primarily trend in the NW-SE direction, with a minor influence from the ENE to WSW. On the other hand, the upper part of the Lower Acacus represents a transgressive wedge, bounded by a surface comprising transgressive. Moreover, the complexity and the interplay between shoreline of this nature poses challenges for traditional 3D porosity and permeability modeling, hindering the accuracy of results. However, the Sand ratio map and Isopach maps confirm the lateral variations of the depositional sequences and the extent of the sand bodies in the target reservoir. The petrophysical model showed that the Lower Acacus Formation had excellent reservoir characteristics in terms of effective porosity and water saturation. To better exploit the most favorable sectors in the Lower Acacus reservoir for accumulation and production of hydrocarbons are likely at the Eastern and southeastern parts of the studied area. The study findings show a strong correlation with established trends, improving the reliability of the models and enabling informed decision-making, including prospective wells.
Use of Natural Language Processing and Computer Vision in Deep Learning for Equipment Failure Investigation on Drilling Tools
Authors: Hutahaean Junko , Simon Kai
Published: February 2025

Incident investigation analysis within the oil and gas industry is a critical process to ensure operational safety, minimize downtime, and improve asset management. However, the sheer volume and heterogeneous nature of data sources (including structured and unstructured text and visual information) present significant challenges to traditional methods of incident classification and contextual understanding and are labor-intensive and error-prone. This paper addresses these challenges by proposing an approach that harnesses natural language processing (NLP) and computer vision techniques in deep learning for equipment failure investigation analysis in drilling tools.

The first component of our approach focuses on leveraging NLP for automated incident classification from a mixture of structured and unstructured text data within the oil and gas industry. With vast volumes of data generated from maintenance logs, technician reports, and incident summaries, manual incident classification becomes impractical and error-prone. By applying advanced NLP algorithms, including text mining and sentiment analysis, we automate the process of categorizing incidents, enabling real-time prioritization and deeper semantic analysis. The second component introduces a novel application of computer vision, where we employ deep learning-based techniques to detect and extract textual information from images captured on various electronic boards. By training models on annotated image datasets, our methodology facilitates the extraction of textual content from diverse electronic boards, enriching the incident investigation process with valuable insights.

Our NLP methodology analyzes the textual content of diverse data sources and enables rapid identification, categorization, and prioritization of critical incidents. By automating text detection from visual electronic board sources, the computer vision model built in this study enhances incident data collection, improves incident context understanding, facilitates efficient information extraction, and facilitates more accurate root cause analysis.

Through empirical validation and case studies, we demonstrate the efficacy and novelty of our integrated approach. Our methodology streamlines incident investigation analysis by automating incident classification and text extraction from visual sources, providing deeper insights into incident contexts, and enabling more informed decision-making. This scalable and effective solution improves incident response, enhances operational safety, and preserves asset integrity within the oil and gas sector, offering a transformative approach to complex incident analysis challenges.
Intelligent Identification of Formation Lithology While Drilling
Authors: Peng C. , Zhang H. L. , Fu J. H. , Li Q. F. , Li J. , Zhu B. , Peng J. P. , Zhang H. R.
Published: February 2025

During drilling engineering, the rate of penetration (ROP) is the basic index to measure the drill ability of various rocks, and drilling parameters are the main control factors that affect the ROP. Identifying formation lithology while drilling can promptly adjust drilling parameters and effectively improve drilling efficiency. In this paper, drilling parameters and intelligent models are combined to realize formation lithology identification while drilling. Different from previous research, this method uses K-means, Fuzzy C-means (FCM), and Mean Shift algorithms to cluster the data set after dimension reduction, uses support vector machine (SVM), random forests (RF), and extremely randomized trees (ET) algorithms to train multiple models to identify formation lithology according to the clustering results, and analyzes the identification accuracy of the models under different combinations of dimension reduction and clustering methods. The results indicate that: 1) without clustering, the accuracy of the model in identifying formation lithology is poor, and the final identification result is biased towards one or two types of lithology; 2) the linear kernel function is the best among the three kernel functions, and the classification results lead to high accuracy in identifying lithology. The classification results of the Gaussian kernel function and polynomial kernel function are biased towards a certain kind of lithology; 3) in the multi-model of linear kernel function + K-Means + SVM, the identification accuracies of sandstone, mudstone, limestone, and shale are 60%, 80%, 70%, and 90% respectively, with an average identification accuracy of 82.5%. This paper puts forward a method for identifying formation lithology while drilling by combining drilling parameters and intelligent models, and takes the YX block as an example to carry out application test. The results show that this method can effectively improve the accuracy of lithology identification while drilling.
Facies Interpretation of Deepwater Deposit in Offshore Sabah Basin
Authors: Charoensuk Jutamas , Centhonglang Chayarus
Published: February 2025

One of the key challenges in deep-water fields offshore Sabah Basin is differentiating turbidite reservoirs from non-reservoir Mass Transport Complexes (MTC). This study integrates seismic and well data to enhance the accuracy of reservoir identification, a critical factor in hydrocarbon resource estimation.

The study began by establishing the geological framework of deep-water deposits through regional basin studies and literature. Facies analysis of turbidites and MTCs was conducted using conventional core data, which was then calibrated with wireline log data (Quadcombo, Dipmeter, and Image logs). The identified facies were tied to 3D seismic data to delineate sequence boundaries, characterize seismic facies, and extract geobodies. Additional inputs from formation pressure tests and field production data further refined reservoir connectivity.

A well-level investigation of core, wireline, and image log data successfully distinguished MTC from turbidite reservoirs. Dipmeter data from image logs provided a clear distinction between the two depositional environments, and density logs highlighted the rapid deposition and de-watering processes associated with MTC, which showed higher density trends compared to turbidite layers. MTCs were characterized by chaotic seismic reflectors and discontinuities, while turbidites exhibited parallel, continuous reflectors. The analysis revealed that low-amplitude, chaotic seismic reflectors, and discontinuous patterns signify MTC, whereas high-amplitude, continuous reflectors correspond to turbidite reservoirs. However, seismic resolution in highly faulted zones posed challenges in separating thin MTC layers from turbidites.

This project offers a robust workflow for distinguishing turbidite deposits from MTC in deep-water settings, significantly improving reservoir model inputs and enhancing resource estimation accuracy. The study also underscores PTTEP's technical capability in addressing complex subsurface challenges in the Sabah Basin.
Adopting Hybrid Cloud Architecture to Enhance Bohai Digital Oilfield
Authors: Wen Xin , Lin Yang , Li Man , Gao Jing , An ChuangFeng
Published: February 2025

Since 2019, with the rise of cloud computing, traditional oil field production management systems have progressively shifted to the cloud. However, this transition encounters various hurdles, including rigid structure, isolated data, scalability issues, and obstacles in technology adoption. To address these challenges, Bohai oilfield has successfully built an intelligent oil field management system using hybrid cloud architecture. This system not only ensures flexibility and expandability but also establishes mechanisms for data integration and sharing, enabling flexible scaling, ensuring compatibility across diverse platforms and devices. Additionally, the intelligent oil field management system adopts a multi-tenant architecture, allowing a single application to serve multiple users or organizations, breaking down data silos, and improving resource utilization.

This paper provides a detailed introduction to the construction of the intelligent oil field platform based on hybrid cloud architecture, including the characteristics of this architecture and its advantages in software development, the design and implementation process of the intelligent oil field platform, and elucidates the interaction mechanism among modules like data collection, real-time monitoring, and intelligent analysis. Furthermore, it describes the application advantages of hybrid cloud architecture in the intelligent oil field platform and validates the effectiveness and feasibility of this solution through some case studies of our intelligent oil field management system, demonstrating its potential in improving oil field production management efficiency and reducing costs. This research not only provides practical references and guidance for the digital transformation of the oil field industry but also offers valuable experience and suggestions for the construction of intelligent oil field platforms.
Perforation Spacing Optimization for Well Integrity
Authors: Younessi A.
Published: February 2025

Small spacings between the perforation tunnels may exacerbate the failure behind the completion by connecting the failure zones in adjacent perforations. These instabilities threaten the integrity of the perforated intervals of the wellbore. It is common to evaluate the stability of perforation tunnels to optimize the perforation orientation, interval and planned drawdown pressure to avoid solid production, but the effect of the perforation spacing is usually ignored. Here, a workflow is presented for optimizing the phasing and shot density of the perforation tunnels to minimize the risk of formation failure in the completion zone. This is done using a series of numerical simulations designed to calculate the zone of impact developed around a single perforation with different orientations and downhole pressure and temperature conditions. Thereafter, a pragmatic approach is used to compare the simulation results against the calculated minimum spacing correspondent to different perforation designs (phasing and shot density). The proposed workflow introduces a practical approach to optimize the perforation phasing and shot density from the formation stability and completion integrity point of view. This geomechanical approach combined with the classic perforation design conducted by production and completion engineers reduces the risks of well integrity failure for its expected life span.
A Clay Stabilizer Technology Replication for Production Enhancement in A Brown Field
Authors: Ramly N. A. , Hendraningrat L. , Riyanto L. , Shafie N. A. , Piah M. F. Mat , Borhan N.
Published: February 2025

Migrating clays are identified as one source of damage in the formation especially in sandstone reservoirs. Particular clay particles usually migrate around the wellbore region where they get trapped, resulting in permeability impairment, and consequently causing production decline. An offshore oilfield was observed to have a sharp drop in oil productivity and successfully resolved and enhanced production with an extremely low decline rate. This paper presents a replication of a clay stabilizer technology to another brownfield in the Offshore Malaysian area.

The analysis was performed with an established methodology as conducted during the pilot in the previous project. It was started from the root cause of formation damage analysis and conducted through integrated laboratory experiments and successfully identified the damage mechanism to migrating clays issue. An in-house clay stabilizer chemical was deployed together with an acidizing job. Numerical modeling is developed to predict the performance of post-treatment. The project risk, mitigation plan, POS, and economics are evaluated as part of a comprehensive project evaluation.

The replication candidate well has good porosity and a permeability but had a low liquid rate, even assisted with gas lift. Evaluation has been done and no well integrity issue but a skin build-up as damage. Based on the mineralogy analysis, clays content is dominated by migrating clays. The chemical compatibility tests have been done in the laboratory including with acid chemical and resulted in no issue on compatibility. The project was started with acidizing stimulation and continued with clay stabilizer injection with the bullheading method. The risk and mitigation plan were observed, and POS is 87% with a high chance of success.

The replication and application of a novel fine migration technology for production enhancement in brown fields represent a significant advancement in reservoir management. By addressing the challenges associated with fines migration, this technology has the potential to improve production efficiency and extend the life of mature oil fields.
Strategic Decommissioning: Global Ultimate Strength Analysis for Fixed Offshore Structures
Authors: Nor M. N. K. Mohd , Zalahan M. F. , Aliumar M. A. , Izhar M. H. , Shafie W. H. F. W , Khai L. T. , Sidek A. M. M , Daud M. H. M , Hasim M. S , Salleh A. B. , Jani A. M. , Rahman A. R. Abdul , Ayob M. S. , Rahman A. A.
Published: February 2025

Decommissioning a fixed offshore platform poses a multifaceted challenge for the operator due to the confluence of technical, economic, and environmental considerations. Technically, dismantling and removing the infrastructure in a safe and efficient manner, particularly in deepwater locations, requires intricate planning and execution. Economically, decommissioning involves substantial costs, and operators face the challenge of managing costs while meeting regulatory obligations. Furthermore, a significant number of decommissioning candidates are aged platforms that have surpassed their designated design life. This ageing factor introduces a critical dimension to the imperative of ensuring safe and effective decommissioning operations.

Many of the platforms identified for decommissioning are aged structures, and already beyond their intended design life. With increased environmental loadings, these ageing platforms will face challenges in meeting current Codes and Standards requirements on Design Level Assessments, particularly in the structural integrity of their substructures. Therefore, strategic planning is essential to ensure safe and efficient execution of decommissioning.

An opportunity emerges during the economic and technical assessment of platform decommissioning. Global Ultimate Strength Analysis (GUSA™) is to be conducted to substantiate the deferral of the platform’s decommissioning. Non-linear Plastic Collapse Analysis is a tool for advanced analysis of structures for risk-based assessment and has been accepted by most of the major operators in the industry. It is also one of the main components in GUSA™. The analysis results present the justifications to address any non-conformance to the Codes and Standards, and strategically plan for offshore decommissioning execution by leveraging the insights gained from the ultimate strength assessment.
A Novel Approach to Chasing the West Balingian Miocene Channelized Reservoir in the R Field Offshore of Sarawak
Authors: Rahman G. , Mun T. S. , Mei P. C. , Phonphetrassameekul Nontnarit
Published: February 2025

The R Field, located within the Balingian Province offshore Sarawak, comprises a complex Miocene channelized reservoir system. Initial exploration success was followed by subsequent appraisal wells encountering less favorable sand thicknesses. To address this challenge, a comprehensive geological modeling approach was employed to characterize the reservoir’s spatial distribution and identify optimal well locations.

The methodology involved integrating seismic inversion, well log interpretation, and outcrop analogs to develop a detailed geological model. Rock physics analysis was conducted to enhance seismic interpretation and improve reservoir characterization. Well correlation and facies analysis were used to establish depositional patterns and identify key reservoir elements, including channel, crevasse splay, and floodplain facies.

A combination of object-based and multiple-point statistics (MPS) modeling techniques was applied to generate multiple realizations of the reservoir’s sand distribution. These realizations provided a probabilistic framework for assessing uncertainty and optimizing well placement.

By integrating diverse data sources and employing advanced modeling techniques, the study successfully improved the understanding of the R Field reservoir. The resulting geological model serves as a valuable tool for guiding future exploration and development activities, mitigating risks, and maximizing hydrocarbon recovery.
Monetising the Forgotten Oil With Machine Learning Assisted Rock Typing In A Giant Brown Field
Authors: Gazali I. M. , Ramly N. , Zakaria A. , Zin W. Wan Mohamed , Nadarajan B.
Published: February 2025

An unsupervised clustering for rock typing definition coupled with a feedforward deep learning neural network have been developed in-house for a giant brown field. This field under study has thick reservoir sediment deposition amounting to 7000 ft with more than 200 reservoir units and close to 200 logged wells. However, cores were only taken in limited selected sands in 5 wells. Owing to the large field size and varying reservoir properties, previous approaches to rock typing have been piecemeal and only catered for specific areas of the field. To overcome these challenges and generate a field-wide rock typing, a machine learning assisted approach has been developed which groups the reservoirs into different rock types by unsupervised clustering guided by Flow Zone Indicators and depofacies, and then subsequently populates these rock types using a feedforward deep learning neural network trained on Gamma Ray, Bulk Density, Neutron Porosity and Total Porosity logs. In a recent drilling campaign, these new rock types have identified several minor reservoirs with good permeabilities which were not previously modelled. Consequently, these missed hydrocarbons or forgotten oil zones were completed as additional upside, resulting in the well delivering more than double its promised production rate.
Quantitative Evaluation of Factors Influencing Particle Migration in Loose Sandstone Reservoirs
Authors: Fan Jiale , Hou Yawei , Cai Hui , Chen Mingyang , Wang Pengfei , Lv Zuobin
Published: February 2025

Bohai A Oilfield is a loose sandstone reservoir. During water flooding development, particles in the reservoir migrates and blocks the oil layer, thereby affecting the development effect of the oilfield. In order to characterize the blockage law of particle transport, an innovative characterization method is proposed. This method uses particle non-analytical CFD-DEM method and microscopic displacement to simulate the particle migration process and mechanism. This method is based on the CFD-DEM particle fluid transport coupling theory technology. The coupling algorithm of the technology takes into account the collision force, viscous force, van der Waals force and charge force between particles. It also includes buoyancy, drag force and Magnus force between particles and fluid. It also includes adhesion force and collision force between particles and porous media. It can realistically restore the entire process of particle transport, blockage, and precipitation at the particle scale. The effects of pore throat shape, ratio of particle diameter to throat diameter, particle concentration, fluid viscosity and pressure gradient on particle migration and retention are analyzed. We get the following understanding: First, when the ratio of particle diameter to throat diameter greater than 0.85, the increase of collision probability between particles and throat wall leads to blockage. Second, as the concentration increases, the retention rate increases. Particle concentration<1%, when the particle concentration is less than 1%, the interaction between fluid and particles is the key factor leading to particle retention. Third, when the viscosity of the fluid is higher (more than 50mpa·s), the particle retention rate increases greatly. Last, The effect of pressure difference on particle migration is comprehensive. It would not only reduce the retention rate of particles, but also increase the speed of particle shedding. This study can quantitatively evaluate the influence of various factors on the migration of reservoir particles, and determine the blocking limits of particles under different influencing factors. It can guide the efficient development of oil fields and provide a new idea for the development of similar loose sandstone reservoirs.
Alternative Scale Clean Out Solution by Automated E-Line Milling in Slimhole Well
Authors: Suwannakarn O.
Published: February 2025

One of the main challenges of gas production well is scale built up which leads to flow restriction around perforated holes in front of the sand face. The scale formation usually starts at the tubing wall but with time accumulation, scale build up finally develops into full bore restriction and prevent wellbore accessibility at deeper section. To remove wellbore restriction, slickline cleanout is the primary method but the success rate is low or in between 19-33%. In order to gain higher clean out success rate then using Coiled Tubing Unit is the secondary option however the investment is significantly higher than conventional slickline unit which make many wells subeconomic to proceed and consequently there are reserve left unproduced. This project has been developed to overcome this challenge by deploying existing technology of milling operation via E-line unit. This technology was proved efficient in many fields, but this is the first time to operate in slimhole well condition.

The first time in the industry with an automated Electric-line (E-line) milling has been deployed in 2-7/8" tubing. Automated E-line milling is the robotic toolstring assembly, automatically controls tractor force with intercommunication between milling cartridge and tractor, embedded with control algorithms. The technology is designed for cleaning out wellbore restriction from scale built up.

In 2023, we have trial E-line milling for 5 wells in Gulf of Thailand (GOT) mature gas field, resulting in total of 935 ft length of scale milled with estimated additional reserve gain at 47% from the total of target reserve but utilize lower cost at approximately at $1.3MM or half of Coiled Tubing Unit cost. Team have experienced many challenges during milling operation including higher temperature than tool specification, cross flow between pay zones, and cutting handling to surface especially when milling scale at the bottommost perforated pay sands. Project has been carried out with real time monitoring and supervised from product champion to discuss and develop immediate actions throughout the project for optimizing milling efficiency. The lesson learns from this trial projects are evaluated and will be applied for next campaign improvement.

Automated E-line milling is an alternative solution of wellbore scale removal for subeconomic wells when considering Coiled Tubing clean out. This technology can minimize cost, unlock reserve recovery, and improve flexibility since lower resources are required. This paper will highlight the result of the trial campaign that firstly implemented in slimhole well with tubing size 2-7/8" with discussion of some operating problems during execution.
Effects of Salinity Contrast Between Formation Water and Injected Low Salinity Water on Low Salinity Waterflooding in Dolostone Formation
Authors: Junput N. , Srisuriyachai F.
Published: February 2025

The use of Low Salinity Waterflooding (LSWF) as Enhanced Oil Recovery (EOR) technique has gained attention due to its cost-effectiveness, simplicity, and environmental benefits. Existing research emphasizes the importance of injected brine salinity and Potential Determining Ions concentration in EOR, primarily in sandstone reservoirs. The aim of this study is to assess the feasibility and effectiveness of applying LSWF in carbonate contexts, addressing a gap in current research. A core flooding experiment was performed to assess the impact of salinity contrast between formation water and injected water on the oil recovery mechanism during LSWF in dolostone formation. According to the study, the combination effect between calcium and sulfate ions yielded benefits in LSWF in dolostone formation. This led to dissolution of magnesium ion while calcium ion was not largely consumed due to neutralization of positive charges of dolostone surface. Nevertheless, the addition of magnesium ion into solution enhanced an effectiveness of solution as magnesium ion could replace calcium ion in calcium carboxylic complex in oil droplets, resulting in more active calcium ion in the system. Based on this study, it was concluded that presence of calcium, magnesium, and sulfate ions had a combined effect that enhanced the oil recovery mechanism through Multi-component Ion Exchange (MIE). Core flooding experiment demonstrated that the overall process required an adequate amount of calcium ion and sulfate ion to decrease the strength between the adsorbed oil layer and external dolostone surface. The optimal salinity contrast ratio for the injection of low-salinity water into formation water in this study ranged from 5.64 to 14.10 when salinity of formation water was 28,196 ppm. This salinity contrast ratio led to an incremental recovery factor (RF) of more than 25% after conventional waterflooding. For a formation water salinity of 56,392 ppm, the ideal salinity contrast ratio was approximately 11.28, resulting in an incremental RF of about 8.6%. MIE might not occur effectively at extremely low or high salinity contrasts, resulting in lower oil production compared to optimal salinity contrast range. The performance of LSWF was less effective at very high formation water salinity compared to low formation water salinity. At very high formation water salinity, the magnitude of improvement or incremental oil recovery was much smaller in all cases. Besides the salinity contrast between formation water and injected water, the magnitude of formation water might be another controlling factor of the process. The ratio of dissolved magnesium ion to consumed calcium ion showed similar results in both high and low salinity of formation water. A moderate ratio of ion replacement occurred at the optimum salinity, resulting in an obvious increment of oil recovery.
Lesson Learned and Practical Guide to Successful Formation Pressure Testing Across Naturally Fractured Carbonate Formations
Authors: Ekpiri E. A. , Alfozan A. M. , Alzayer M. F. , Algherairy M. S.
Published: February 2025

Naturally fractured carbonate reservoirs present a multitude of challenges that require innovative approaches in drilling and data acquisition especially when severe mud losses are encountered from both natural and induced fractures. These losses require further drilling considerations to ensure safety of the well. Also, acquiring accurate formation pressure data poses additional challenges due to requirements such as stationary time during the acquisition process. In this paper, we discuss the challenges and lessons learned in acquiring formation data and also develop a decision matrix for advancing a formidable pressure data acquisition program under hostile borehole conditions.

A detailed analysis of all formation pressure testing techniques recently applied to acquire formation pressure is performed to identify the advantages and limitations associated with each technique. These techniques include Wireline, Pipe Conveyed Logging (PCL) and Logging While Drilling (LWD). The analysis evaluates the performance of each technique when deployed in the challenging borehole conditions in the presence of solid debris from LCMs, recent mud losses, and differential sticking risk.

The study provides a comprehensive decision matrix which when applied resulted in selecting Logging While Drilling technique in washdown mode as the most suitable technique. Furthermore, the technology was successfully deployed following a customized approach to acquire open hole formation pressure data in conditions where conventional acquisition techniques like Wireline, Pipe Conveyed Logging and LWD failed. LWD formation pressure testing on washdown mode with the ability to control testing parameters on-the-fly via mud pulse downlinking based on stickiness limitations at every required station significantly reduced risk associated with differential sticking that was observed in the preceding openhole Wireline logging run. The approach also eliminates additional downtime risk that comes with latching operations while using Pipe Conveyed Logging technique.

The generated decision matrix is a practical tool supported with results from real life experience in formation pressure acquisition techniques beyond available conventional approaches. It also provides confidence in the deployment of washdown LWD formation pressure testing in a non-conventional way to acquire critical reservoir data in a hostile borehole environment. By integrating novel approaches with established methods, the paper aims to inform optimal pressure measurement strategies in challenging reservoir conditions.
Trapped and Movable CO2 in Geologic Carbon Storage: Deep-Learning Forecasting and Generalization Study
Authors: Ren G. , Talabi O. , Kumar V. , Misra S. , Odi U. , Silver A. , Gupta A.
Published: February 2025

Geological carbon storage (GCS) is crucial for reducing greenhouse gases and mitigating global warming. Deep saline aquifers are regarded as optimal sites for implementing GCS. This paper proposes an LSTM-based deep learning model that can rapid forecast the temporal evolution of the trapped and movable CO2 in the subsurface aquifer during a GCS process. Rapid forecasting enables agile decision-making by providing timely insights into rapidly changing environments. The training and testing datasets consist of 1,600 simulations modeling the evolution of CO2 in saline aquifers under various geological and engineering parameters. Additionally, an inference dataset of 440 simulations with parameter values different from those in the training and testing dataset was used to evaluate the model's generalization capability. After training and testing, the model can forecast the dynamic temporal changes in the mass fractions of residually trapped and solubility trapped CO2, as well as the mobile CO2 over an 80-year period, involving both injection and monitoring phases. The prediction results demonstrate that the LSTM-based deep learning model not only achieves a Coefficient of Determination (R2) of up to 0.99 and low Mean Absolute Error (MAE) but also requires merely 0.35 milliseconds to forecast the movable and trapped CO2 mass fractions, representing a computational speed-up of six orders of magnitude compared to traditional simulators. The model's predictive accuracy on the inference dataset consistently achieved an R2 of above 0.96, indicating strong generalization capability in predicting complex combinations of geological parameters.
Extending the Lifespan of Marginal Field Through in-Situ Gas Lift in Sarawak Offshore
Authors: Dasuki N. A. , Islamov R. , Anand S. , Utta A. M. , Saleh N. E. A. , Bidin L. A. , Mahamad M. N. A. , Subari I.
Published: February 2025

A marginal field, projected for a 5-year production as part of a fast-track Field Development Plan (FDP), was established utilizing two wells. The wells were designed without gas lift capabilities, based on the anticipation of increased gas production. However, the actual gas-oil-ratio (GOR) diverged from the projected simulations, resulting in lower gas output. This discrepancy, coupled with decreasing reservoir pressure and increasing watercut, has led to challenges in sustaining the wells' production. To address these challenges, the implementation of In-Situ Gas Lift (IGL) application has emerged as a strategic solution to restore production.

The wells were completed with access to the gas zone and L reservoir, with side pocket mandrel positioned near the gas zone. To activate the gas zone, the operator performed well modelling to determine the optimal gas requirement, factoring in additional gas rate for Greenhouse Gas (GHG) management. The well models were updated to accurately reflect the latest well performance, with sensitivity analyses performed to explore optimization options. Integrated Network Modeling (INM) was also utilized, playing a crucial role in identifying strategies to enhance overall field performance and maximize both individual well productivity and network-wide efficiency.

The operator successfully revived two wells, achieving production rate of 2,100 bopd and securing 3.5 MMstb in reserves through IGL. Each well is anticipated to benefit from 0.3 – 0.4 MMScf/d in of IGL from the gas reservoir. For future planning, based on an integrated well-by-well review workshop, the operator has identified additional opportunities behind casing of multiple gas zones to provide lifting assistance. This is especially crucial for future wells planned for field development, which will also rely on IGL. IGL emerges as a cost-effective, robust solution for improving oil recovery in marginal fields, leveraging reservoir gas to ensure production stability. Its flexibility and low operational costs make it an ideal solution for optimizing production efficiency and extending the lifespan of assets in challenging operational environments.

This paper delves into the unique challenges faced and strategic approaches taken to extend the productive life of the field using IGL. It highlights the need for adaptability and innovation required in field development, especially when there are significant discrepancies between initial projections and actual production performance. The insights gained from this study offer valuable lessons for managing similar fields, emphasizing the critical role of continuous monitoring and flexibility in adjusting strategies within dynamic production environments.
The Transport Mechanism of Proppant in Rough Fractures in Unconventional Reservoir
Authors: Jin Ting , Dong Yifan , Yang Shengfang , Zhang Yuelong , Wang Lei , Shao Yinan , Zhang Ning
Published: February 2025

Proppant distribution within fractures is a crucial factor in enhancing the production from fracturing. Currently, most studies on the transport and settling behavior of proppants in fractures are based on the models of smooth, straight fracture. There is rarely research considering the effects of fracture tortuosity and roughness on proppant movement and settling. To further reveal the trends of proppant transport and setting in tortuous and rough fractures, physical experiments were conducted to observe proppant behavior in smooth and flat fractures firstly. Based on this, established tortuous and rough fracture models to explore proppant movement and settling patterns. Results indicate that larger proppant particle sizes are more conducive to settling within smooth plate fractures, and medium-to-large particle proppants are more likely to accumulate near the branch fracture entrances. For smooth-plate fractures, the increased the injection rates will allow proppants to penetration deeper into both main and branch fractures. For tortuous rough fractures, larger proppants are more impacted by fracture structure, often accumulating in the middle sections and causing blockage; however, increased injection speeds can effectively mitigate these blockages. These findings reveal the influence of various operational factors on proppant transport and settling, offering theoretical guidance for optimizing hydraulic fracturing techniques.
Countdown To CCS - The Monitoring And Surveillance Of High CO2 Sarawak Offshore Field
Authors: Manaf M. A , Dasuki N. A. , Bidin L. A. , Othman A. I. , Shata K. A. , Berok S. M. James , Hameed J.
Published: February 2025

An offshore gas field with a high CO2 content was developed with the plan of storing the CO2 in a nearby depleted field as a Carbon Capture and Storage (CCS) initiative. The performance and therefore production data of the field is crucial to ensure the overall success of the project. Thus, before the commencement of CCS, a robust monitoring and surveillance plan of the field’s performance and contaminants must be in place.

To achieve this objective, a 5-year plan of monitoring and surveillance was devised specifically for the high CO2 field. Additionally, a literature review of Monitoring, Measurement and Verification (MMV) for CO2 storage in general, as well as a review specific to the storage site, were extensively conducted. Finally, an Integrated Asset Management (IAM) platform for real-time monitoring of well and equipment parameters, was built for the field to facilitate faster analysis and troubleshooting.

For the high CO2 field, a Data Acquisition (DA) outlook which included but not limited to Static Gradient Survey (SGS), Pressure Build Up (PBU), reservoir saturation logging, and wellbore inner diameter evaluation was planned that could help with field performance and integrity analysis. From the literature review on MMV, the storage site must have special considerations of monitoring and surveillance due to the high CO2. For the daily monitoring and surveillance, the IAM proved to be invaluable to give real-time data and timely intervention.

The monitoring and surveillance of the high CO2 offshore field are using both the conventional as well as customized tools and techniques. This is to ensure robust preparation for the CCS project and can be a benchmark for similar endeavors in the future.
Intelligent Diagnosis of Electric Submersible Progressive Cavity Pump Based on Multiple Parameters
Authors: Ma Gaoqiang , Shi Junfeng , Chen Xuehui , Lan Fang , Zhao Ruidong , Zhang Xishun
Published: February 2025

The Electric Submersible Progressive Cavity Pump (ESPCP) is an innovative rodless lifting technology that uses cables to propel downhole PCP, effectively reducing issues like rod-tubing deflection and improving transmission efficiency. While traditional methods rely on specialized ground electronics for assessing ESPCP working conditions, these methods often lack precision for accurate diagnosis in intricate conditions. The ongoing trend of well digitization has led to ESPCP wells equipped with ground electronics and downhole multi-parameter acquisition systems. However, academic discussions have not adequately explained the influence of these parameters on working conditions. This study addresses the challenge of low diagnostic accuracy in relying solely on ground electric condition and the absence of diagnostic technology utilizing multi-parameter data. A 10-parameter sensitivity analysis model is developed to incorporate ground electric current, downhole temperature, pressure, and vibration. The research elucidates the interplay of multiple parameters under varying working conditions, identifying 6 key control factors including oil pressure, casing pressure, rotational speed, power, pump suction pressure, and pump temperature. By conducting fourth-order partial correlation analysis, the study quantifies the impact of each parameter and the primary correlation parameters across different operational scenarios, establishing a comprehensive multi-parameter synergistic change model for various working conditions. Focusing on the main correlation parameters of typical operational states, temporal characteristics are extracted from time, frequency, and time-frequency domains to serve as input data for a Long Short-Term Memory (LSTM) neural network model. This intelligent diagnostic approach for ESPCP wells, based on multi-parameter working condition analysis, has been successfully implemented in 50 oilfield wells. The diagnostic accuracy has significantly increased from less than 80% to over 92%, demonstrating the potential for precise control of ESPCP wells and extending pump detection period.