bluuebunny/crossref_metadata_2025 huggingface dataset:
A Comprehensive EOS Model for Extra-Heavy Oil Blended with Liquid Solvents: Development, Assessment and Validation
Authors: Al-Murayri M. T. , Akther S. , Al-Otaibi B. , Abu Taleb R. , Al-Busari A. , Delamaide E. , Soltani A. , Garifullin R. , Bekri S. , Rousseau D.
Published: April 2024

Work is in progress to de-risk and mature liquid solvent injection for extra-heavy oil extraction from the Lower Burgan Abdali (ADBG) reservoir in North Kuwait. This is a deep reservoir (i.e. 9,000 ft) that is not believed to be amenable to steam-based EOR methods. This paper presents a workflow to create a holistic equation of state (EOS) describing the thermophysical properties of the selected solvents, extra-heavy oil, and their blend at different conditions. Such a model is essential to capture subsurface-to-surface variations in the mixture properties, thus allowing proper assessment of solvent-assisted oil production to design a fit-for-purpose pilot for further validation.

Two multi-component liquid solvents and representative extra-heavy downhole oil samples were investigated using high-pressure laboratory testing. Different conditions were considered to measure fluid and density. Thereafter, a cubic EOS model was built to reproduce the extra-heavy oil lab-based PVT data. On a parallel track, another EOS model was also developed for the two selected solvents, namely: diesel and Solvarex. These two EOS models were merged into a unified model to generate reliable extra-heavy oil-solvent mixture predictions. A validation technique was suggested to verify the impact of lumping and pseudo-component overlapping on oil recovery and injected fluid breakthrough.

Significant differences in the molecular weight of C36+ and the measured viscosity were observed through lab evaluation using representative downhole oil samples with negligible dissolved gas. Upon creating a single EOS model using multi-fluid regression, it was realized that the heaviest fraction had to be split into two pseudo-components, coexisting in both samples, but at different molar fractions. The components existing in extra-heavy oil and solvent were lumped mainly to avoid overlapping. The resulting EOS model was found to be robust as it can accurately reproduce the phase and volumetric behaviour of extra-heavy oil, solvent and their blend at the reservoir temperature and different pressures.

Furthermore, it was established that, with such a robust EOS model, an arbitrary number of pseudo-components can be defined for the crude oil and solvents PVT models, without a considerable impact on oil recovery or injected fluid breakthrough.
Study on Digital Twin Technology to 3D Seismic Iterative Processing and Geological Modeling for Reservoir Development
Authors: Yang Xuesong , Cao Bin , Wang Huilai , Zhou Wei , Zhang Xu , Li Jiye , Li Chenya , Zhang Bingxuan , Huang Xingning
Published: April 2024

For an already established 3D geologic model, the accuracy of model correction only by seismic and well data is not high. Seismic-driven modeling, a modeling method that fully integrates seismic data, can fully explore the potential information in seismic data and convert seismic residuals into corrections of model errors through seismic driving to realize the updating and correction of the model. Based on this, this paper proposes the seismic forward loop iterative model correction technique and describes the technical process of this method with the example of the actual work area. The qualitative and quantitative evaluations of the model correction results are carried out by seismic comparison and well comparison techniques, and the results show that this method can reduce the cumulative error of the model correction, make the model closer to the real value, and better characterize the non-homogeneity of the reservoir and the distribution law of natural fractures. Based on the shale gas production well area of Changning Company, this project focuses on the research of different natural fracture prediction and the reliability evaluation method of the prediction results and explores the influence of different natural fractures on drilling and fracturing to form a more perfect and effective prediction and evaluation system of natural fractures in deep shale gas. The research results will guide the prediction of natural fractures in shale gas production zones, which will help improve the scientificity and adaptability of the design of engineering parameters in study area.
Field Development Plan (FDP) Cloud-Based Data Integration Between Geology & Drilling Software Using Python Tools
Authors: Mwansa P. , Repilloza H. , Hassan M. A.
Published: April 2024

The oil and gas industry generates vast amounts of data throughout its operations, from exploration to production. Collecting, connecting, and optimally utilizing this data is key to maximizing efficiency, accuracy, and access to new disruptive technologies.

In a typical well-planning cycle, an engineer will spend a significant amount of time looking for the data they require to do their jobs efficiently. The data are typically locked away in silos - trajectories in one data platform, Pore Pressure Gradient, Fracture Gradient or Targets in another, and so on.

A major Middle Eastern NOC and Two Norwegian software service companies teamed up to develop Proof of Concept (PoC) for a new workflow that integrates subsurface and drilling data between on-premises Geology E&P software and Drilling software through a proprietary Python Tool plug-in and Python library. This integration enables a streamlined connection to a cloud-based drilling and well planning software, facilitating collaboration among teams involved in well planning. The project's key challenges are the lack of a standardized communication, integration, and automation of data flows between subsurface and drilling teams, as well as the inability of engineers to access necessary data due to scattered information and access restrictions. The project utilizes a proprietary data science suite, named Cegal's Prizm, which allows easy configuration to integrate data from various applications, sources, and platforms. A proprietary Python Tool is used to merge data from various application silos and data sources, enabling enriched investigation. The process involves connecting to the Geology E&P software retrieving domain objects using the proprietary Python Tool, and converting these domain data objects into common Python data structures. The project aims to develop an innovative workflow that provides easier access to data for experts throughout the organization, leading to better decision-making during the well-planning cycle. This not only makes it easier, but it also ensures collaboration between the G&G and Drilling teams involved in new well planning
Novel WAG Method with Nanotechnology in a Heterogeneous Carbonate Reservoir
Authors: Jang H. C. , Ko S. M. , Park H. R.
Published: April 2024

In the WAG process, adding nanoparticles to the water phase (referred to as NWAG) is a method advantageous for enhancing oil recovery and CO2 storage by improving wettability. Previous research had limitations in not reflecting actual site conditions, as it did not account for heterogeneity and hysteresis. However, most reservoirs exhibit different permeability distributions, and CO2 can be trapped due to hysteresis. Therefore, it is essential to consider petrophysical properties, such as heterogeneity and relative permeability hysteresis, for accurate predictions. In this study, a 3-D heterogeneous reservoir model with a relative permeability hysteresis model was designed. Based on this model, a sensitivity analysis of the NWAG method was conducted for each factor to investigate its impact on oil recovery and CO2 storage. This analysis was performed using the CMG-GEM simulator, and the results were compared with the findings obtained for each different factor. A Dykstra-Parson coefficient of 0.4 was selected to represent the reservoir's heterogeneity. Furthermore, the capillary trapping of CO2 during WAG injection was calculated using Larsen and Skauge's three-phase relative permeability hysteresis model. The sensitivity analysis considered factors such as injection fluid-gas ratio, slug size, injection period, injection cycle, and nanofluid concentration. The simulation results indicated that the heterogeneity and hysteresis model have an impact on oil recovery and CO2 storage. Specifically, when considering both heterogeneity and hysteresis, oil recovery and CO2 storage exhibited differences of 0.8% each. In the simulation model that incorporated heterogeneity and hysteresis, the difference in oil recovery was 0.8%, while the difference in CO2 storage was 15.2%. Comparing nanofluid injection to CO2-WAG, it was observed that nanofluid injection can alter wettability by reducing permeability. This mechanism suggests that the use of nanofluids can enhance oil recovery and expand CO2 storage, resulting in an efficiency improvement of up to 5% in both oil production and CO2 storage when compared to the conventional WAG process. The NWAG method can offer new insights into the highly effective exploitation of oil reservoirs. Considering a limited supply of CO2 from industry and a constant source, the NWAG method is expected to be efficient way to enhance oil recovery and increase CO2 storage capacity.
Fit for Purpose H2S and Oil Removal System for a Sustainable Zero Liquid Discharge (ZLD) Produced Water Management Technology
Authors: Ayirala S. , Saleh S. H. , Ahmed S. , Haider S. , Yousef A. , Saeed Q. , Muhammad M. , Saif Suha , Gendron Blake , Akram A.
Published: April 2024

The study focuses on Zero Liquid Discharge (ZLD) produced water desalination, an increasingly important process for recycling hypersaline wastewater and promoting a circular water economy in upstream operations. A key challenge in ZLD desalination is pre-treating produced water streams to remove hydrogen sulfide (H2S) and oil, as H2S is a harmful toxic gas and oil can cause operational issues in desalination units.1,2

The study aimed to develop and evaluate a fully automated pre-treatment system for removing H2S and de-oiling produced water streams. This system included a series of chemical dosing, integrated degassing, and de-oiling unit, and scrubbing units. The degassing unit, using a controlled nitrogen supply, efficiently removed H2S from the water. Chemical dosing was employed to aid in removing H2S, and oil, while the scrubbing unit targeted residual H2S in the vapor phase.

Results indicated that the automated system was highly effective in both H2S removal and de-oiling, achieving up to 99% efficiency in both processes. Key factors contributing to this efficiency were the amount of chemical dosing, the residual time, and the controlled nitrogen supply in the degassing unit. The study demonstrates the feasibility of using a fully automated pre-treatment system for H2S and oil removal in ZLD produced water desalination projects. This system is particularly useful for treating sour produced water in both remote and populated locations.

Additionally, the study highlighted the system's scalability to accommodate varying water volumes and its capability for in-line monitoring of treated water and scavenged H2S. This versatility allows real-time optimization of chemical loading, making the system a novel solution for H2S and oily produced water treatment. However, further research is necessary to optimize the system for different produced water streams and assess its long-term efficiency.
Produced Fluid, WTP and BFW for Steam Generation Management at Mukhaizna Heavy Oil Field
Authors: Al-Asimi M. , Al-Habsi A. , Al-Qalhati A.
Published: April 2024

The Mukhaizna heavy oil field in Oman is one of the world's largest steamflood operations. With more than 3,000 wells drilled (producers and injectors), gross fluid production has surpassed 900K bbl/day, with steam injection of 600K bbl/day and water disposal of 300K bbl/day after 17 years of successful steamflooding.

These large volumes of produced fluids undergo various treatment processes to prepare them for either sale, boiler feedwater for steam generation, or disposal. With water cuts exceeding 90% and fluid chemistry changing with new fluid streams added from exploration areas, technical difficulties compelled a multidisciplinary team to share operational intelligence to optimize the processes of dehydration, de-oiling, water treatment, and disposal.

To address the challenges posed by the substantial volumes and high temperatures of viscous produced fluids, free water knockout vessels, skim & dehydration tanks, and induced gas flotation are used, enhanced by a carefully optimized selection of chemicals, resulting in a reasonably efficient dehydration and de-oiling processes.

The water treatment plant (WTP) uses a sophisticated treatment process to comply with the specifications for boiler feedwater. This process involves chemical precipitation softening methods, ion exchange with strong acid cation (SAC), a reverse osmosis membrane, and thermal desalination using one of the world's largest Mechanical Vapor Compressors (MVC).

The high volumes of multiple disposal fluids demand a complex and ecological disposal system. The current process includes conventional deep subsurface disposal and segregated evaporation ponds. Other environmentally friendly solutions are being considered, including autonomous inflow control devices/valves and the use of reed beds for wastewater treatment.

. Due to the operation's size and complexity, the team examined leveraging modification of the facility to improve produced flu id cost and to recycle selective waste streams. This approach would alleviate bottlenecks, resume production of shut-in high-water-cut wells, optimize chemical expenditures, and enhance the water quality entering the WTP's SAC inlet.

The team has recommended both mechanical and chemical solutions to unlock the full potential of these mature assets, leading to savings of more than 20% on some chemicals and a big gain in production from re-opening the shut-in wells.
Synergistic Integration of a Photovoltaic Park and an Electrolysis Facility for Sustainable Green Hydrogen Production
Authors: Oprisescu-Dulgheru Andrei-Stefan
Published: April 2024

In today's quest for energy optimization and reduction of carbon emissions, hydrogen has emerged as a promising solution for capturing and storing surplus energy. With the increasing production of renewable energy leading to negative energy prices, hydrogen plays a pivotal role in efficiently utilizing this surplus energy. This research presents an advanced and comprehensive model for the synergistic integration of a photovoltaic park and an electrolysis facility, aiming to harness excess energy and produce sustainable green hydrogen.

The integration model revolves around the utilization of an advanced Maximum Power Point Tracking (MPPT) Charge Controller. This controller plays a vital role in optimizing energy extraction from the photovoltaic park by ensuring that the solar panels operate at their maximum power point, thereby enhancing the overall efficiency of the park. Furthermore, during periods of high solar energy production, the surplus energy is intelligently directed to the electrolysis facility instead of being wasted or negatively impacting energy prices.

By coupling the photovoltaic park with the electrolysis facility, the surplus energy is effectively converted into hydrogen through the process of electrolysis. This conversion allows for the storage of potential energy in the form of green hydrogen, which can be utilized for various applications, including power generation, transportation, and industrial processes. The production and utilization of green hydrogen not only facilitate energy storage but also contribute to the reduction of carbon emissions, promoting sustainable and environmentally friendly energy practices.

The proposed integration model addresses the challenges associated with surplus energy management and offers significant advantages. By effectively utilizing surplus energy through hydrogen production, it mitigates the financial implications of negative energy prices and maximizes the utilization of renewable energy resources. Furthermore, the adoption of green hydrogen as an alternative to conventional fossil fuel-based energy sources supports the global transition towards a low-carbon future. By efficiently managing surplus energy and harnessing the potential of hydrogen as an energy carrier, this integration model not only optimizes renewable energy utilization but also promotes a more sustainable and environmentally conscious energy landscape.
Ocean Bottom Node Seismic Data Changes Occidental's Plan for Lucius Field in the Gulf of Mexico
Authors: Barnett D. B. , Manning J. P.
Published: April 2024

Geological risks and uncertainties must be considered when evaluating and deciding whether to drill an offshore prospect. Recent technological advances in seismic data acquisition, processing and interpretation techniques have provided geoscientists cleaner data sets and new tools to help make such decisions. This paper will highlight the technological advances of deep-water seismic data at Lucius and the value gained through optimizing the field development plan.

The advent and application of Ocean Bottom Node seismic acquisition has greatly improved subsurface imaging and helped geoscientists reduce geological risks and better quantify subsurface uncertainties. Occidental has recognized the value of OBN seismic surveys, having employed it recently with immense success across multiple Gulf of Mexico assets. These new surveys, along with innovative processing tools such as Least Squares RTM, Full Waveform Inversion, and attribute analyses, have greatly improved how geoscientists measure the risk and uncertainty of prospects.

Learnings from the OBN survey at the Lucius field in the deep water GOM have helped Occidental optimize the field development plan by shedding light on the structural complexity of new fault blocks, better defining the salt/sediment boundary, and improving reservoir characterization. These learnings drove changes in and additions to future drilling locations and identifying possible stranded hydrocarbons.

Investing in seismic technology is often overlooked in mature assets, but at Lucius, the OBN data set has led to more integrated and robust interpretations and has already delivered to co-owners over four times the return on investment. OBN implementation has provided a step change in subsurface understanding.
A Systematic Approach to Multistage Hydraulic Fracturing Optimization for Production Improvement with Geomechanical Modelling
Authors: Jia Jing , Wang Lichang , Zhang Xin , Fan Wenli , Meng Xianhang , Zhang Hanyue , Yu Yanbo , Yang Chunxi , Huang Xingning
Published: April 2024

In order to advance the sustainable development strategy of oil fields and cope with the crude oil prices fluctuations, the oilfield operators adopt a risk-sharing development mode of project contracting and EUR sharing for inefficient blocks with high barrel oil costs to reduce the investment risk. Focused on "single well production enhancement" and "cost control", improve the accuracy of geological and engineering sweet spot predictions via geology and engineering integrated study. Given the non-homogeneity and non-uniformity of hydraulic fracturing of unconventional reservoir, the application of hydraulic intelligence neural network technology is crucial. Fully leverage fracture information obtained from imaging logging and the reflection of various seismic attributes on the fracture to quantitatively study the distribution of fractures. The evaluation of the characteristics of stress, geomechanics, and the interaction between hydraulic and natural fractures are essential for more accurate simulation and prediction of the hydraulic fracturing. Via conducting hydraulic fracturing simulations based on differential stress distribution in horizontal wells, optimizing parameters such as fracture spacing, length, height, cluster combination, and pumping scale to achieve the best match between engineering technology and reservoir conditions, ultimately realizing efficient development.
Using a Genetic Algorithm to Estimate Bingham Equation Parameters for Rate of Penetration Prediction
Authors: Eltrissi Mohammad , Yousef Omar , El-Banbi Ahmed , Khalaf Fouad
Published: April 2024

Bingham rate of penetration (ROP) model offers a reasonably accurate and easy-to-compute equation for ROP prediction. However, the model requires estimating the local empirical parameters varying by region and formation encountered. This requirement may cripple the effective utilization of the model when new entrants drill in a location. The standard method for estimating these empirical parameters is by iterations to find the optimum values.

This work presents a methodology for estimating localized modified Bingham equation parameters using the Genetic Algorithm (GA). The case study was conducted on a large dataset collected from 260 wells covering the range of formations in the Western Desert of Egypt. The dataset provides more than 390,000 records for twenty-eight different formations in two different geological basins in the Egyptian Western Desert. The data was pre-analyzed by two different data processing techniques prior to its introduction to GA.

The parameters of the modified Bingham model for every formation were determined using the entire dataset. The outcome range of the dataset prediction error ranges between 5.3 m/hr and 26.1 m/hr, with an average error of 13.91 m/hr for all 28 formations. In addition, it was observed that the model had increased accuracy with depth despite some anomalies that could be found in a few formations. The resultant empirical parameters for the modified Bingham ROP model of the case study can be used for any Egyptian Western Desert ROP prediction application, while the introduced algorithm can be applied elsewhere to find the value of its local empirical parameters.
Decarbonizing Drilling Operations Using a More Efficient Power Source, Electrical Utility Power
Authors: Landry B. , Trueheart L. , Stopkoski C. , Vaughan D. , Dotson D. , Landacre K. , Benedicti P. , Posse P. Riva
Published: April 2024

Electrification has proven to be an effective way to lower operational costs and reduce the carbon footprint of drilling operations. This paper provides a technical analysis of highline (grid) power utilization on drilling rigs through the lens of an innovative technology that connects a rig to a power grid, reducing Scope One emissions to nearly zero.

Traditionally, diesel engines and generators have been used to produce electricity, both direct and alternating current (DC and AC) for drilling operations. This power source is effective and reliable, but it has drawbacks that can be addressed using highline electrical power, which is more affordable, efficient, and clean.

Recently introduced technology allows drilling operations to be powered by an electrical utility grid using a substation transformer to step down highline power voltages to rig voltage and interface the utility grid with the rig's powerhouse. An electrical transfer switch enables the rig to switch between highline and generator power quickly and easily. In situations where grid power cannot be utilized, the highline transformer module allows the existing power generation system to remain integrated to the drilling rig. The system is compatible with both Variable Frequency Drive (VFD) and Silicon Controlled Rectifier (SCR) drilling rigs and has minimal impact on rig move timelines.

Powering the drilling rig with electricity instead of generators decreases on-location emissions and noise levels to near zero, and highline power can reduce the overall carbon footprint of drilling operations even more when utility power is created by more efficient and lower polluting methods such as natural gas power plants, nuclear power, or renewable energy (solar, wind, geothermal, etc.). Field data collected from more than 20 highline transformer modules deployed on rigs across the Lower 48 United States demonstrates an average daily reduction in Scope One emissions of 20 metric tons of CO2e per rig.

With highline powered operations becoming more commonplace in the United States, technological advancements are starting to make their way to global markets.
Optimizing Well Placement in Heterogenous Carbonate Reservoir Utilizing a New Generation of High Definition Mapping Tool
Authors: Al Araimi A. , Al Yahyai H. , Taher A.
Published: April 2024

Field development plans and oil recovery can be significantly enhanced by the full understanding of the geological structure, petrophysical properties and reservoir behavior. Detailed subsurface understanding can depict strategies for reservoir and field development. Building a detailed picture of the reservoir requires a combination of measurements and data acquisition processes usually starting with seismic data to build a geological model which is refined during drilling operations with a combination of logging while drilling (LWD) and reservoir mapping sensors.

Conventional LWD sensors usually measure formation properties around the well trajectory, Deep azimuthal electromagnetic (EM) tools are often used to map boundaries further away from the wellbore to improve well placement. The resistivity of the surrounding formation and resistivity contrast with the adjacent layers are two critical factors that influence the depth of detection and ability to map multiple layers. As the resistivity contrast decreases, it becomes more difficult to identify boundaries at a distance. A Deep Azimuthal EM tool with enhanced sensitivity to resistivity variations was deployed along with a mapping inversion algorithm in the case study. In addition to utilizing density and neutron tools, to provide a detailed picture of the reservoir and refine the geological interpretation near well-bore.

A case study from the North of Oman where a carbonate reservoir with anticipated thickness variation and variable reservoir quality, overlain by shale identified from offset data was selected for the deployment of a high-definition multi-layer inversion mapping tool. The varying thickness of the carbonate target and the small contrast in resistivity laterally towards the toe with the shale above imposed additional challenges for conventional deep resistivity tools due to their limited depth of detection and, were anticipated as being a challenge to the well placement operation, limiting the depth of investigation and the ability to detect the critical boundary and ensure there was no exit in areas of thinning.

The Realtime inversion output showed the Resistivity degradation across the lateral section ranging from 12 ohm-m to 2.5 ohm-m, the thickness of the reservoir showed substantial variation varying from 32ft at heel to 8ft at toe. The continuous detection of bed boundaries along with lateral deterioration of resistivity provided a detailed view of the reservoir thickness and dip variation to optimize well placement decisions A novel approach for utilizing a new generation of high-definition multi-layer inversion mapping tool, the successful mapping of the boundaries in low resistivity contrasts expands the environments in which the technology can be confidently deployed and ensure optimum well placement with increased reservoir understanding.
Machine Learning Model for Efficient Surveillance of Electrostatic Pump Operations & Predictive Analytics for Early Alarming System
Authors: Alrowayyeh Jarrah , Dhote Prashant , Alqenaei Abdulmohsen
Published: April 2024

In today's fast-paced technological world, Oil and Gas E&P industry must embrace innovative solutions for boosting efficiency and minimizing expenses. One promising direction involves investigating smart systems that can supervise, notify, and foresee the malfunction of electrical submersible pumps (ESPs). By leveraging artificial intelligence (AI) and machine learning (ML), we can transform how we handle the upkeep and administration of these vital elements. Adopting AI and ML-driven solutions offers significant advantages, such as preventing expensive failures and promoting eco-friendly resource management. By detecting and resolving potential issues early, we can extend equipment life and reduce our operations’ environmental footprint.

The widespread application of Electrical Submersible Pumps (ESPs) in Kuwait Oil Company (KOC) has recently experienced a surge in pump failures due to increased water production from maturing fields. This issue prompted the exploration for adopting intelligent systems, and integrating AI and IoT, to improve ESP monitoring and predictive maintenance, ultimately enhancing efficiency and reducing costs. The study employed a novel workflow connecting field, well, and detailed ESP operational data, such as intake and discharge pressure, wellhead and flowline pressure, motor current and voltage, motor temperature, reservoir temperature, and vibration for failure diagnosis. Predictive analytics were utilized to proactively identify potential issues before they manifested, thereby mitigating the impact of these failures.

The study results show that utilizing advanced technologies such as Machine Learning and Automatic Surveillance Systems can significantly reduce ESP failures. Key conclusions from the analysis include,

A streamlined solution is provided to identify and predict ESP failure causes. ESP real-time data is digitized and converted into a computationally friendly form. Early anomaly detection enhances well uptime and minimizes ESP failures. Model parameters can be fine-tuned for better results with a successful proof of concept (POC). The proposed workflow aids in cost reduction for workovers and maintains production. Decision-making is improved through the monitoring system.

This approach offers an accessible and user-friendly platform for non-specialists within organizations. Oil companies can adopt this workflow to boost well performance and pinpoint areas for improvement. Ultimately, implementing digital transformation will elevate organizations’ competitiveness in the global industry.
Evaluation the Effect of Temperature and Reaction Time on Hydrothermal Conversion of Oil Shale: Insights into Synthetic Oil Generation and Micro-Scale Pore Structure Change
Authors: Saeed Shadi A. , Al-Muntaser Ameen A. , Hakimi Mohammed H. , Kadyrov Rail I. , Morozov Vladimir P. , Abdullah Mohammed A. , Suwaid Muneer A. , Nguyen Hung T. , Varfolomeev Mikhail A. , Nurgaliev Danis K. , Al-Fatesh Ahmed S. , Nazimov Nafis A.
Published: April 2024

This research presents a systematic study on the hydrothermal conversion (artificial thermal maturity) performance of oil shale from Domanik Formation (Russia). The experiments were conducted at different conditions including temperatures of 300°C, 350°C, and 400°C, and reaction times of 1, 3, 6, 12, and 24 hours. For this purpose, the quantity and quality of produced synthetic oils, the composition of released gases, as well as changes in pore size and structure of shale consolidated core samples by X-ray computed tomography were evaluated. In addition, SARA group composition, carbon number distribution including light fractions (C10 – C20, with a boiling point of 216–342°C, representing petroleum, kerosene and diesel cuts) and heavier fractions (C21 – C32, with a boiling point of 356 – 458°C), elemental analysis, and the compositions of obtained synthetic oils (including aliphatic and aromatic hydrocarbons) and the conversion degree of kerogen were analyzed using multiple techniques including, Low Field - NMR, GC, GC–MS, and Rock-eval pyrolysis. The obtained results confirmed that the high quality of synthetic oils is characterized by a higher amount of light fractions (C10 – C20) compared to heavy fractions (C21 – C32), with a higher amount of saturate and aromatics than resins and asphaltenes. The degree of OM conversion varies depending on the temperature and reaction time. Among 300, 350, and 400 °C, the highest conversion rates were 25, 70, and 99% for reaction times of 24, 24, and 6 h, respectively. Hydrothermal conversion at 300°C for 24 hours, 350°C for 24 hours and 400°C for 3 hours showed the highest quality of synthetic oils. Notably, hydrothermal treatment caused a substantial change in pore size, leading to an improvement in porosity and the formation of connected pore channels. The porosity increased approximately 4 times (from 2.6 to 11.9%). In general, the promising obtained results technically demonstrate the feasibility of developing Domanik oil shale by in-situ hydrothermal retorting.
Vision Inspection of Power Lines with Deep Learning
Authors: Alotaibi Najd , Dursun Serkan
Published: April 2024

The purpose of this technical paper is to introduce a novel approach to inspecting power lines using computer vision and deep learning algorithms. Traditional inspection methods are often time-consuming and costly and can be dangerous for human inspectors. This paper presents a new workflow for power line inspection that leverages machine learning algorithms to automate the process.

The proposed workflow for vision inspection of power lines involves capturing high-resolution images of power lines using drones or other unmanned vehicles. These images are then processed using a deep learning algorithm, which is trained to identify potential condition such as good, problem, and unknown for various components in the power lines. The workflow utilizes cutting edge object detector models, such as YOLOv5 and YOLOv8 to analyze the images, and output the prediction of bounding boxes, abjectness scores and probabilities for each detected object.

This paper presents a novel approach to power line inspection that utilizes deep learning algorithms to identify and localize objects not only by using axis-aligned bounding boxes but also with oriented bounding boxes, which can significantly reduce the time and cost associated with traditional inspection methods. The use of drones and other unmanned vehicles also increases safety by eliminating the need for human inspectors to climb power line structures.

The vision inspection workflow presented in this paper has the potential to revolutionize the power line inspection industry. By utilizing deep learning algorithms and unmanned vehicles, inspection times can be reduced, costs can be lowered, and human safety can be improved. This approach can also lead to more efficient maintenance and repair, prolonging the lifespan of power lines and reducing the risk of power outages. Future research can explore the potential of using this workflow for other types of infrastructure inspection as well.
Construction of Mini Gas Processing Plants in the Conditions of Autonomous and Hard-To-Reach Oil Fields to Extract Gas Processing Valuable Components and Improve the Environment by Minimizing Gas Flaring
Authors: Chernoyarov Alexander
Published: April 2024

Currently, associated petroleum gas (APG) utilization, in order to minimize flaring, at oil and gas fields is solved by several classical methods:

Pipeline transportation of gas to consumers or large gas processing / chemical plants. LNG production and transport to the consumer. Utilization at oilfield power stations.

For autonomous oil and gas fields, the issue of gas utilization is especially acute if there is no infrastructure for transporting gas and its products to consumers, taking into account the economic feasibility of the project, in view of the high capital costs for classical gas processing plants.

Typically, classical gas processing plants are designed and constructed in large-tonnage scale, with a long feedstock profile, more than 15 years, to ensure the profitability of the project, due to the high cost of gas equipment. Often, the transportation of semi-products, such as NGL, from autonomous, hard-to-reach and small oil and gas fields is also unprofitable. Regarding this issue, APG utilization has to be carried out directly at the field by unprofitable methods - partial combustion, power generation, etc.

At the same time, APG is a valuable raw material in terms of obtaining products such as NGL, propane, butane and their mixtures. APG flaring has negative effects on the environment in the form of CO2 and other pollutant emissions, in this regard, the involvement of APG in processing even at small and autonomous fields allows the most efficient use of natural gas resources and improves the environment in the region.

Gazprom Neft Company implemented a mini gas processing plant with a capacity of ~ 4.25 MMSCFD for the processing of rich APG with the NGL production at an autonomous oil field, which made it possible to reduce APG flaring and send valuable raw materials (NGL) for processing together with oil. A separate infrastructure for the NGLs transportation was not been realized. NGL is transported by injection to on-spec oil, considering the RVP restriction. This decision helped to eliminate capital expenditures on the infrastructure for the NGLs shipment and transportation, which ensured the economic viability of the APG utilization project.

Mini Gas Processing Plant (NGL recovery) was implemented in modular form, which made it possible to place column and heat exchangers equipment with a minimum plotplant.

As a result of the mini GPP project implementation, a high APG utilization rate at the field was achieved - 99%.
Electrical/Electromagnetic Enhanced Oil Recovery for Unconventional Reservoirs: A Review of Research and Field Applications in Latin America
Authors: Rodriguez Fernancelys , Belhaj Hadi , Morales Raifel , Velasquez Richard , AlDhuhoori Mohamed , Alhameli Fatima
Published: April 2024

The exploitation of unconventional oil reservoirs with low environmental impact represents a big challenge nowadays, where considerable efforts are being focused on the production of clean fuels with low carbon content, and the energy transition to the net-zero carbon emissions. Electrical/electromagnetic Enhanced Oil Recovery (EEOR) processes have been considered emerging techniques that allow increasing mobility of crude oil along with reduced and low environmental impact, compared to well-known and established thermal techniques such as steam injection where significant volumes of water are required for steam generation, coupled with the high production of acid gases during these processes.

This article presents an exhaustive review of the Electrical/electromagnetic EOR methods evaluated at laboratory scale or investigated by numerical simulations, mostly undertaken for deeper understanding of the associated physical phenomena, as well as specific field-scale applications for several unconventional reservoirs in Latin America. In this context, the main challenges associated with the implementation of the process mechanisms and the application of these technologies along with the evaluation of applicable ranges of related critical parameters will be highlighted, and the main conclusions of laboratory experiences or/and field condition cases will be captured in this article.

Among the main results of this review, it is found that the application of Electrical/electromagnetic EOR/IOR methods offers advantages in increasing the mobility of crude oil without generation of high-water cuts as well as reduced H2S production, both having significant low impact environmental considerations. However, the common operational challenges and/or issues of these EOR techniques are related to process inefficiency due to the presence of high-water saturation, reservoir heterogeneities, electrical leakage, mechanical failures, tool's material characteristics, location of the heating, gas breakthrough and management of greenhouse gases, which pose limitations on the wide-spread use of these heating methods.

The paper will discuss important underlying aspects of the electrical/electromagnetic EOR processes to evaluate the viability of applying these methods in unconventional Latin American reservoirs, emphasizing Venezuelan reservoirs, based on technical and economic evaluations worldwide, targeting production of fossil fuels with low environmental impact from the huge hydrocarbon reserves located in Venezuela, mainly in the Orinoco Oil Belt.
A Digital Framework for the Integration of Simulation Prediction Profiles
Authors: Ciaurri D. Echeverria , Aqeel N. , Aldolijan W. A. , Salamah M. S.
Published: April 2024

In the oil and gas industry, vast numbers of simulations are commonly utilized to evaluate production scenarios for field development and business analysis and optimization. However, the process of compiling the obtained prediction profiles and of performing the required analysis is often carried out manually, which is not only time-consuming but also prone to errors. In this work, we present a framework aimed at automating the streamlined generation and analysis of the aforementioned profiles within a fully digital environment.

The workflow introduced conducts a comprehensive analysis of the input and output simulation files for each scenario. This includes temporal analysis and processing, such as the reporting of well drilling and activation, and installation of artificial-lift systems over time. Standardization of certain features is crucial to ensuring smooth automation. The framework allows for concurrent evaluation of multiple scenarios, making it suitable for distributed computing. It is important to note that profile archiving facilitates the creation of interactive dashboards for subsequent analysis and optimization. These dashboards can prove to be valuable tools in achieving desired business outcomes.

The proposed methodology was implemented using Python to extract and manipulate generic simulation input and output data and generate the prediction profiles. A standardization stage was performed beforehand by collaborating with the involved teams. The profiles had to be outputted in a pre-specified format and were subject to a series of validation tests. In all cases, the generated profiles were successfully validated. The computation time for a single profile, which typically takes several hours or even days, was reduced to approximately one hour and sometimes even less than that. It should be noted that parallel computing of all profiles can introduce an additional speed-up factor, reducing the wall-clock time frequently by at least one order of magnitude. The profiles were saved in a repository where data visualization and business-intelligence software could be utilized to extract various types of information for sound and robust decision-making, for example, through recommender systems that optimize capital expenditures and other costs.

The digitalization, integration and analysis of simulation output from a large number of scenarios can be a rather challenging enterprise due to the sheer volume and diversity of data generated by simulations. To overcome these challenges, advanced data analytics can be applied within a high-performance computing environment to extract meaningful insights and drive effective decision-making, enabling companies to stay competitive in an increasingly dynamic and uncertain market.
Advance Control System and Optimization OTSG MPC & Optimizer
Authors: Al Shueili H. H. , Asgharzadeh R. S. , Al Alawi R. , Al Adawi A.
Published: April 2024

The Mukhaizna field in the Sultanate of Oman is a leading EOR in heavy oil recovery, where innovative strategies are continually sought to optimize production and minimize environmental impact. Among these strategies, the optimization of Once Through Steam Generators (OTSG) plays a pivotal role. This paper presents a comprehensive study on the implementation of an advanced control system and optimization strategies for OTSGs in Mukhaizna, aiming to enhance operational efficiency and environmental sustainability.

In recent years, the oil and gas industry has increasingly focused on reducing carbon emissions and optimizing resource utilization. The Mukhaizna field, characterized by its unique geological structure and operational challenges, provides an ideal setting to explore these objectives. This study outlines the deployment of a Model Predictive Controller (MPC) and Bank Optimizer, tools designed to maximize the thermal efficiency of OTSGs. The primary goal was to optimize steam quality and quantity, reduce fuel gas consumption, and, consequently, lower CO2 emissions.

It currently necessitates approximately 630K barrels of steam per day to satisfy its extensive steam flooding requirements. This substantial volume of steam is a critical element in the enhanced oil recovery efforts, ensuring the viability and longevity of the field's production capabilities. The generation of this steam is achieved through a combination of 13 Heat Recovery Steam Generators (HRSG) and 78 Once Through Steam Generators (OTSGs) arranged in Thermal Banks. This setup represents one of the field's most significant operational factors, underpinning the need for optimized control and management to maintain production efficiency and cost-effectiveness. The following discourse details the development of an advanced adaptive control system tailored to enhance the operation of these OTSG units, underscoring our commitment to operational excellence and environmental stewardship.
Geosteering and Monitoring Methods for Underbalanced Coiled Tubing Operations in Gas Reservoirs
Authors: Alsaud S. Abu , Katterbauer K. , Alshehri A.
Published: April 2024

Under-Balanced coiled tubing drilling (UBCTD) is one of the techniques used in developing low pressure gas reservoirs. However, a drawback in UBCTD is their limited geosteering and monitoring tools due to the coiled tubing size compatibility and hole dynamic condition. As such, we present a review of the current available geosteering and monitoring technologies that will help in achieving optimum geo-navigation through the gas sweet spot layers. In this work, a review of current geo-navigation and monitoring technologies for UBCTD operations will be demonstrated. After defining the associated challenges of steering with UBCTD, different technologies will be outlined based on their applicability and effectiveness in landing the UBCTD laterals in the sweet spot layers. These technologies are going to be in the form of downhole logging while drilling (LWD) tools (gamma ray and resistivity), real-time surface measurements, as well as cutting analysis like X-ray diffraction (XRD) and X-ray fluorescence (XRF).

Based on a thorough analysis of the different measuring methodologies, each one has its advantages and disadvantages, as follows: (i) The gamma ray measurement helps determine the lithology through the naturally occurring elements of the layers, but its reading doesn't effectively detect gas bearing zones. (ii) The resistivity reading helps in distinguishing conductive with non-conductive; however, good resistivity contrast between the layers needs to be present in order to distinguish layers that cannot be detected from the gamma ray alone. (iii) Gas flowback while drilling is an excellent indicator of gas presence, yet it doesn't directly indicate productive zones for better steering. (iv) Utilization of cutting analysis is the closest direct measurement of the reservoir, but its issue comes from its subjectivity, sample quality, and inconsistency in cutting depth correlation. As a result of the comparisons, we concluded that a combination of all the techniques is essential in achieving optimum well placement with UBCTD, where some technology weaknesses can be tackled with the other strengths.

This review represents a method of utilizing existing geosteering and monitoring technologies to advance UBCTD reservoir navigation capabilities by integrating different LWD, real-time measurements, and cutting analysis to achieve optimum lateral placement, yielding better field development and increased gas recovery.