bluuebunny/crossref_metadata_2025 huggingface dataset:
Study on the Mechanism of Carbonate Formation Strength Degradation Induced by Water-Rock Interaction
Authors: Zhang Dujie , Li Daqi , Jin Junbin , Tu Yulin , Rached Rached
Published: February 2024

The strength deterioration of the wellbore rock induced by water-rock interactions would be an important cause of the delayed instability in the Leikoupo formation located in Western Sichuan Basin, Sichuan Province, China. Taking the carbonate rock sample obtained from the Leikoupo formation as the research object, this study systematically analyzed the composition and the microstructural characteristics of the rock firstly. Then, a series of rock mechanics experiments before and after immersion in deionized water, pH=9, pH=11, and water-based drilling fluid (pH=11.5) was conducted. In order to further analyze the microscopic mechanism, the change in the ion concentrations of the immersed fluid and rock microstructures were analyzed. The results indicated that the Leikoupo formation carbonate rock was mainly limestone with developed micro-fractures. After immersion in alkaline solution, the elastic modulus and compressive strength of the rock decreased obviously. The friction coefficient of the fracture surface decreased as well. The degree of the deterioration became more significant with the increasing solution pH value and the prolonged immersion time. The analysis suggests that the dolomite in the carbonate rock undergone de-dolomitization reaction in high-temperature alkaline solution. It dissolved the micro-protrusions of dolomite on the fracture surface, so as to reduce the surface roughness. The research findings preliminarily reveal that the dissolution-crystallization-expansion mechanism of de-dolomitization reaction was the driving mechanism for the deterioration of carbonate rock due to water-rock interactions, providing a theoretical basis for the development of drilling fluid technology for stable wellbore in fractured carbonate rock formations.
Performance Evaluation of Autonomous Outflow Control Devices in Various Injection Wells, Field Data Review of the Technology Deployments
Authors: Moradi M. , Kvilaas G. F. , Konopczynski M. R.
Published: February 2024

The important role of optimized injection wells has been recognized as a crucial element for optimum field development and enhancing oilfield recovery factors. The functionality of most applied technologies to provide optimum injection conformance has been limited as the properties of injection wells are continuously changing. This paper will present an overview of oilfield production data for using outflow autonomous devices and demonstrate their benefit in various well injection applications by optimizing the well injectivity.

The devices have been installed worldwide including in the Middle East, Norway, North America and China. Like other flow control devices, this autonomous device was installed in several zones in wells and has two operating conditions, one as a conventional flow control device, and two, as a barrier when the flowrate through the valve exceeds a designed limit. At the second flow condition, the denied fluid to that specific zone will be distributed among the neighboring zones. This prevents excessive fluid injection into the thief/fracture zones and maintains a balanced fluid distribution across the full targeted injection zone.

This paper describes the completion design workflow and field data including injection logging tool (ILT) and DTS and step-rate and injectivity test data when available for the cases.

The field results have proven the success of autonomous outflow control devices both in terms of achieving the target injection profile and controlling the thief zones. The objectives of installations include controlling faults, natural and thermal fractures, and high perm zones for injection of water, gas and polymer in both carbonate and sandstone reservoirs. For example, in one well the production logging results from data post and before installation confirmed that the flow control devices were successful in controlling the water injection into the suspected thief zones by 42%. Another example in polymer injection well supported by Distributed Temperature Sensor (DTS) data as well as other surveillance data, it was confirmed that 93% of the anticipated conformance has been achieved and the water cut from the oil producers nearby drastically dropped resulting in less production of unwanted fluid and therefore more sustainable oil production.

The paper will provide an elaborate showcase reviewing field data for deployment of autonomous flow control devices in injection wells and evaluate the resulting enhanced well injectivity performance and hence overall field development efficiency. The outflow control device enables operators to deliver optimum water, polymer, acid and gas injection techniques resulting in more sustainable production while avoiding costly and often complex well interventions.
Navigating the Seas of Sustainability: A Study on Maritime Scope 3 Emissions Through Digital Innovation
Authors: Vedat Kris , Neal Benjamin , Eldred Morgan , Sonne-Schmidt Carsten , Suboyin Abhijith , Thatcher Jimmy , Rehman Abdul
Published: February 2024

This case study provides a simplified overview of Scope 3 emissions within industry, focusing on a global offshore logistics provider. With total emissions from offshore logistics projected to increase by 250% by 2050 as per IMO, reducing emissions without impacting productivity and operational efficiency is a key challenge.

The assessment used industry-standard methods and procedures and involved a thorough process of data collection and analysis. This includes leveraging industrial data to conduct preliminary analysis and estimations. The detailed calculations were also based on assumptions and emissions factors from various sources such as GHG Emissions Calculator, IEA, US EPA, ICCT, IMO, and ORR. The comprehensive assessment included emissions from its supply chain to end use of sold services. Categories include purchased goods and services, capital goods, fuel and energy-related activities, upstream transportation and distribution, waste generated in operations, business travel, employee commuting, upstream leased assets, downstream transportation and distribution, processing of sold products and use of sold products.

The company found that its Scope 3 emissions accounted for a significant portion of its overall GHG footprint, and identified several key areas where emissions reduction efforts could be focused. These areas included optimizing shipping routes, improving the energy efficiency of its vessels, and collaborating with suppliers to reduce emissions across the supply chain. These optimizations combined can lead to significant reduction (12 – 25%) in environmental impact in long term along with savings in costs, time, and even potential application in other industrial operations. The findings of this study can assist entities to identify its major emission sources and develop strategies to reduce its carbon footprint.

Our case study offers a simplified overview of Scope 3 emissions, with calculations for each category, aimed at enhancing understanding of emissions categories and calculations for Scope 3. The findings can benefit policymakers and regulators by highlighting areas where offshore logistics can focus their efforts to reduce their Scope 3 emissions.
Water Saturation Profile Analysis and Modeling: Case Study in a Tight Carbonate Reservoir
Authors: Mukhtar Sardar Owais , Khan Mohammad Rasheed
Published: February 2024

The complexity in carbonate reservoir evaluation stems from their intricate pore topology and mineralogical classifications. Classical approach to determine fluid distributions is through resistivity logs, however, this is marred by drawbacks as the resultant water saturation (Sw) can sometimes exhibit a profile that has limited to no usage as it is modeled with porosity and lithological volumes only. Significant uncertainties in Sw calculations from logs in heterogeneous tight carbonate rocks combined with the difficulties in measuring reliable pressure gradients, render the interpretation of transition zones and free water levels challenging. This case study describes the integration of limited core capillary pressure (Pc) measurements, formation pressures, conventional and Nuclear Magnetic Resonance (NMR) logs to define a saturation height model that explains the Sw profiles of a tight gas carbonate reservoir.

The log derived Sw and its associated uncertainty have been computed using input logs and electrical parameters from cores. The NMR log is at the center of the saturation modeling. It integrates the pore size distribution into a synthetic Pc, establishes the irreducible water saturation by adjusting a variable T2 cut-off, and defines a permeability transform adjusted to core data and adapted for wells with conventional logs data only. The pressure gradients analysis, core and synthetic Pc and the petrophysical properties determined from logs have ultimately been combined to define a Saturation Height Function (SHF) for all the wells.

The Sw profiles and the differences in free fluid level positions between the wells cannot be explained by structural or stratigraphic compartments. The assumption of undisturbed initial saturation conditions has been questioned, and further supported by the initial pressure data that could involve earlier production from nearby field. The porosity permeability and Swi estimation from logs are coherent with core data including routine and special core analysis. A key observation was the mismatch of transition zone from core Pc data relative to the Sw profile and Free Water Level (FWL) from gradient analysis; therefore, Sw profile is believed to be more reliable. Uncertainty in Sw calculations exists at high Sw, however, it seems that residual gas exists below the "current" FWL. Moreover, GWC in some wells cannot be explained with the existing reservoir compartmentalization.

This study reveals how the analysis and modeling of the Sw profile can change the understanding of reservoir compartments and field history and have significant impact on its development. A major concern highlighted is regarding those fields having wells at original saturation conditions as if there is a common deeper FWL and variable swept areas leading to different contacts. Finally, relating poro-perm rock typing to lithofacies association and depositional environment can be advanced through core descriptions and thin sections.
Proppant Pack Stability Enhanced by the Combination of New Fiber Technology and Resin- Coated Proppants
Authors: Salazar Franck , Artola Pedro , Bruno Salguero , Delgado Byron , Aguirre Santiago , Peñaherrera Luis , Boas Jacqueline , Ormaza Nancy
Published: February 2024

Hydraulic fracturing has been used for many years as an effective stimulation method in high- and medium-permeability formations where, in contrast to low-permeability formations, the objective is to create a highly conductive proppant pack that bypasses the formation damage. Proppant flowback can affect the stability of the proppant pack and lose the required conductivity in the near-wellbore area. The present work shows how the proppant flowback was significantly decreased by the combination of new fiber technology and resin-coated proppant (RCP) without affecting the productivity index.

Many methods to prevent proppant flowback have been used successfully, but when challenging production conditions are encountered in high- and medium-permeability hydraulically fractured wells, these methods have been proven ineffective. Low closure stress, high flow rates, high pressure drawdown, multiphase flow, high-viscosity fluids, and stress cycling are some of the conditions that favor proppant flowback. The combination of new fiber technology with RCP has been demonstrated to be effective in controlling the return of proppant in wells that were hydraulically fractured by an operator in Ecuador. The new fibers increase the frictional forces between the chemically resin-bonded proppant pack grains by creating a random network of fibers around the proppant grains. The proppant pack stability is increased by the combination of the two mechanisms. The fibers provide additional particle-to-particle mechanical bonding due to friction increase at the contact point, and the RCP provides chemical bonding.

Initially, wells were treated only with RCP but as more challenging conditions were encountered, RCP by itself became ineffective; then, the new proppant flowback fibers were introduced. The new fibers provided better adhesion between the fibers and the proppant, which significantly increased the proppant pack stability and created a long-lasting conductive proppant pack. The combination of the two methods was implemented starting in 2022 in 15 wells in the Oriente Basin in Ecuador. These wells have reported no proppant flowback under harsh production conditions like stress cycling and high production rates in intervals with few perforations. The incorporation of a more stable proppant pack has allowed the operator to flow back the wells at higher initial flowback rates. This has resulted in a cleaner fracture proppant pack and higher post-fracture production rates.

The combination of RCP and new proppant flowback fibers provided maximum flowback control without affecting the productivity index. The addition of the fibers provides better proppant pack consolidation, and this starts as soon as the fracture closes without requiring waiting for the resin bonding activation by temperature.
Mesophase Technology: An Efficient Breaker System for Thick Sludge Removal and Well Productivity Enhancement
Authors: Alabdulmohsen Zainab , Al-Taq Ali A. , Alsalem Ali , Anioke Chidiebere
Published: February 2024

Sludges and emulsions are common and challenging problems encountered in the near-wellbore area that can adversely impact productivity. Sludges, characterized by their thick, semi-solid mixtures of organic and inorganic deposits, have the potential to accumulate in wellbores, restricting the flow of hydrocarbons and leading to a partial or total loss of well productivity. Mesophase is a patented technology designed to efficiently break down sludges, emulsions, and other organic damage encountered in the near wellbore, thereby mitigating production impairment challenges. This technology is based on microemulsion science, offering high detergency and ultra-low interfacial tension properties. It contains organic acid to dissolve acid-soluble materials, facilitating effective chemical treatment—an intelligent, cost-effective fluid solution with minimal health, safety, and environmental (HSE) risks.

The laboratory work conducted in this study included emulsion and sludge breaking tests, sludging tendency testing, surface and interfacial tension measurements, and contact angle measurements. Analytical analyses, including XRD and SARA (saturates, aromatics, resins, and asphaltenes), were performed to characterize different sludge samples examined in this study. Acid-induced emulsions and sludges were generated using 15 wt% HCl and an oil sample. The impact of the presence of iron ions in the acid system was examined at 3,000 mg/L ferric ions. The lab results revealed that the sludge samples contained mixtures of organic and inorganic materials, including substances like iron sulfide and asphaltenes. The results showed that Mesophase fluid was effective in preventing acid-induced sludge and breaking/dispersing different types of sludges. The contact angle results showed that Mesophase fluid had the capability to interact with both water-wet and oil-wet surfaces, indicating its effectiveness in dealing with organic and inorganic sludges.

This paper presents a novel technology aimed at addressing the pervasive issue of heavy sludge formation in the oil industry worldwide.
AI HSE Monitoring & Digital Value Stream Mapping for Pre-fabrication in Sustainability Project
Authors: Shatiry M Sahir Ahmad , Ros Ikhranizam M , Matlan M Norman , Mazny D. E. Farah Shafiqah M , Chelliah Navaneethan , Asmadi M Noraznan , Chappell Jeremy Spencer , Rahim Zulhasni Abd
Published: February 2024

In construction project management, the most critical part is monitoring and tracking construction productivity. The measurement of productivity in a previous way manually where the process and workers’ productivity are recorded and then analyzed. With the rapid rise of the Industrial Revolution 4.0 (IR 4.0), the application of digital technology will further enhance productivity and continuous process improvement. There is various digital technology available during this IR 4.0; among the technology potentially adopted in the Oil and Gas (O&G) construction is digitalisation through Internet of Things (IoT). With the availability of digital solution in the market, the research study to understand and investigate the application of Digital Value Stream Mapping (DVSM) to monitor and track construction productivity. After that, the investigation was conducted to develop the Digital Value Stream Map (VSM) framework for productivity monitoring and tracking for construction work in Oil and Gas (O&G) activity. Then, to validate the developed DVSM framework through expert interviews and case studies of the actual implementation of the DVSM. The application of the DVSM will come out with the analysis of the existing process flow, then to come out with the future state of the process flow by eliminating the waste of labour and material. Finally, the DVSM model success then the study recommends that the DVSM can further expand to utilize at the offshore environment for tracking and improving the overall construction productivity during the hook-up and commissioning phase for Brownfield and Greenfield projects.
Enhancing Reservoir Potential Assessment with Machine Learning Techniques: A Systematic Approach in The X Field
Authors: Suwandi Albertus , Gaol Ardhi Hakim Lumban , Chandra Steven
Published: February 2024

Due to the limited resolution of well logging instruments, particularly older generations, and the presence of complex multiple hydrocarbon systems in a basin, pay zones may be overlooked during measurement and analysis of reservoir characterization. Moreover, while proven and standing the test of time, the conventional approach to stratigraphic correlation and the trial-and-error method for reservoir zone identification are tedious, time-consuming, and expensive. This study proposes a systematic, automated, and accurate reservoir prospect assessment, or reassessment, methodology augmented by machine learning algorithms. The case study investigates S Formation in the X Field, which is marked by a high water cut and the presence of discontinuous parasequence sets. Two machine learning models will be modeled and tuned to X field characteristics. The first machine learning model will assess reservoir stratigraphic relationships, where an unsupervised machine learning model will analyze patterns from log data to geologically describe the reservoir. This can help detect rock formations such as tight shales that conventional interpretation may miss. Third-party stratigraphic data will validate the model results. The second supervised machine learning model will identify hydrocarbon-producing zones. This machine learning model will use resistivity log data and potential zone information from the first model to detect hydrocarbon zones. Swab test data and stratigraphic analysis will confirm machine learning model predictions. The first model successfully captures the stratigraphic patterns of the formation, which aligns with geological validation. The best-performing first machine learning model was able to classify three different clusters of facies, namely shale, sandstone, and shaly sands. The sequence of rock strata and stratigraphic correlations between wells were shown to be consistent with the conventional analysis, with only a minor deviation (0.5-2ft) in the well-to-well correlation sand markers. The second model accurately identifies potential zones based on the given features. The best-performing machine learning model was able to identify the initial condition of the reservoir interval with an accuracy of 84%. Moreover, it could identify a total of 346 potential zone intervals under the same condition. Moreover, as a result of the reservoir re-evaluation using the proposed workflow that has been carried out, a total of 35 candidates for primary perforation and 70 candidates for secondary perforation are proposed in the X-Field. These proposed candidates highlight the results of the workflow implemented to maximize production in the X-Field.
Metaverse: Accelerates Upstream Growth with the Power of Immersive Collaboration
Authors: Al-Harbi Badr , Al-Readean Muhammad , Al-Ghamdi Amell , Al-Akeely Abdullah
Published: February 2024

The global transition towards sustainable future influenced industries to create a fundamental shift in the way they conduct the business to improve sustainability. This paper presents a comprehensive immersive platform that enables multidisciplinary teams and management to make decisions. It connects professionals to collaboratively demonstrate and share findings, run cross-functions business processes in an intuitive way capitalizing on artificial intelligence and cognitive capabilities. All of this is under a unified platform that we called the Upstream Metaverse.

There are four pillars for the Upstream Metaverse: digital assets, real-time collaboration, intelligence, and venues. First digital assets, which include visualization of reservoir simulation static and dynamic properties (e.g., porosity, permeability, water, oil saturation, etc.), other datatypes such as core and well log data. Second is the collaboration and interactive capabilities that allows engineers to be immersed into their models, and run different scenarios. Third is the intelligent layer, which capitalizes on available Industrial Revolution 4.0 (IR4.0) solutions. Fourth is the collaboration venues represented by the Upstream collaboration centers to maximize the user experience.

A synthetic model was used in this paper to demonstrate well placement process, which is one of the Upstream complex processes as it involves many stakeholders. Upstream Metaverse streamlined the process by integrating the key data, and workflows needed for the decision process in a single platform. It enables multiple intuitive visualization methods of well logs to address the complexity. In addition, it provides the visualization of structured data including reservoir models that can be cross-referenced with well logs. Moreover, it provides seamless integration with an intelligent voice assistant to enrich the user experience. These capabilities allow multidisciplinary teams to run different business scenarios, conduct effective field reviews, and collaboratively discuss their findings in an interactive and immersive platform. This allows real-time collaboration around the data for all participants. Furthermore, coupling Upstream Metaverse with digital transformation solutions provides a rich platform that is capable to effectively facilitate the meetings. Also, capitalizing on the integrated data sources, and cross-organization functions combined with streamlined IR4.0 processes enabled on spot decision making.

The Upstream Metaverse established the main building blocks that are fully integrated with data repositories to address current and future challenges. This will directly increase the efficiency of conducting Upstream cross-functions operations through a single, unified, immersive, and collaborative platform. Upstream Metaverse is a sustainable environment that reduces the need for sequel meetings, supplemental silo efforts, and computation resources.
Considering Shear Dilution and Threshold Pressure Gradient Heavy Oil Reservoir Nonlinear Numerical Simulation and Residual Oil Characterization
Authors: Gao Yue , Ma Kuiqian , Liu Jing , Ling Dennis , Yang Bin
Published: February 2024

A large number of experimental results show that, the flow of heavy oil in porous media does not conform to Darcy seepage law. Heavy oil has the characteristics of threshold pressure gradient and its viscosity decreases nonlinearly with the increase of velocity. Heavy oil is a non-Newtonian fluid, but currently commercial reservoir numerical simulation simulators (ECLIPSE, CMG) do not have nonlinear numerical simulation function for heavy oil. In this paper, a new Hg Equation is introduced to match the nonlinear relationship between heavy oil viscosity and velocity, and a new heavy oil nonlinear reservoir numerical simulator is developed considering the threshold pressure gradient and shear thinning characteristics of heavy oil.

Based on the basic data of the SZ Oilfield, this paper established a well group conceptual numerical model, and used the newly nonlinear numerical simulator for heavy oil to study the effects of the threshold pressure gradient and shear thinning of heavy oil on the laws of remaining oil distribution and development characteristics. The numerical simulation results show that the remaining oil distribution law of the model considering the nonlinear seepage characteristics of heavy oil is very different from the normal model, which is reflected in the lower sweep coefficient and the lower remaining oil saturation of the mainstream line. Based on the numerical simulation results, this paper proposed an effective pressure gradient to evaluate the sweep range of heavy oil in the reservoir and an effective well distance calculation method is proposed. In the development practice of SZ oilfield, this method has achieved very good application results.

The novelty of this paper is that considering the characteristics of threshold pressure gradient and shear thinning of heavy oil, a new nonlinear reservoir numerical simulator for heavy oil is developed. The Hg equation is proposed to match the nonlinear change of heavy oil viscosity with shear rate. Based on the numerical simulation results a new calculation method of effective well distance is proposed.
Machine Learning Classifier for Detecting Non-1D Features Using Deep Directional Resistivity Measurements
Authors: Zhong X. , Jith J. , Chang Y. , Gremillion J.
Published: February 2024

Currently, most geosteering jobs can rely on 1D inversion to accurately capture the properties of multiple planarly layered formations in real time. However, when complex non-1D features are present in the formation, higher dimensionality inversions will be required, which need intensive computation resources and more data channels containing 3D information sent to the surface. When abnormal results from 1D inversion are observed, switching telemetry rates and starting higher-order inversions is usually late, which inevitably delays the real-time geosteering decision.

To address this, we have developed a machine learning classifier capable of early detection of non-1D features in the formation before the drill bit reaches the higher dimensional feature. A large number of synthetic formations were generated with and without non-1D features that included faults, water coning, and sand injectites, etc., and then fed into a 3D forward model to generate the deep directional resistivity measurements. We trained the classifier to distinguish between 1D and non-1D cases with high accuracy, specifically employing a multilayer perceptron classifier, which is both accurate and suitable for downhole deployment.

In practice, this classifier can signal the presence of non-1D features, dynamically select telemetry channels, and initiate specialized inversions in real time. Synthetic and field testing validated its effectiveness, achieving over 80% accuracy when the tool is even 10 m from a non-1D feature, with increasing accuracy as the tool approaches the feature. Field results aligned with geological interpretations, affirming the model's robustness and accuracy.

This innovation enhances the ability to recognize complex formations during drilling, automating non-1D inversions and improving bandwidth management in real time, which ultimately increasing drilling speeds. This is one step in the journey towards autonomous geosteering.
Enhancing Predictive Maintenance in an Oil & Gas Refinery Using IoT, AI & ML: An Generative AI Solution
Authors: Saboo S. , Shekhawat D.
Published: February 2024

Oil and gas refinery operations are under constant pressure to enhance efficiency and ensure uninterrupted processing. The adoption of predictive maintenance strategies has emerged as a pivotal solution, enabling real-time anomaly detection, predicting pressure fluctuations, and monitoring asset health. An illuminating example hails from a downstream operator in Western Australia that strategically harnesses the power of IoT and AI/ML. For them, revenue hinges on the streamlined delivery of gas processing services to customers, amplifying the significance of process efficiency gains. Leveraging on-site equipment data analysis, this approach significantly minimizes on-site maintenance requirements and automates back-office tasks, reducing manual data analysis and response generation in maintenance permit systems. The technical infrastructure involves wireless sensor-enabled data collection transmitted to a centralized hub, where machine learning algorithms detect equipment defects. Rapid reporting of these defects to decision-makers, accompanied by contextual insights, empowers swift, informed decision-making. This innovative solution has expanded business horizons, enabling the processing of gas for external entities alongside producing their reservoir gas in the downstream processing plant.
Analytical and Numerical Investigations of the Impact of Anisotropic Elastic Properties on Hydraulic Fracture Geometry
Authors: Shawaf A. , Dehdouh A. , Rasouli V.
Published: February 2024

This study investigates the impact of transverse isotropic vertical (TIV) characteristics on fracture geometry, spacing, and stress shadow development in shales. Shales exhibit transverse isotropic characteristics due to their rich organic content and laminated depositional environments. The lamination planes are horizontal in shale formations having a symmetric axis vertical to the bedding plane. Neglecting the TIV nature of shale formations leads to erroneous in-situ stress estimates, resulting in inefficient fracture design and reduced recovery. The study employs analytical modeling and numerical simulations to analyze the effects of TIV medium properties. Analytical modeling shows that Young's modulus anisotropy significantly impacts fracture width, while Poisson's ratio has minimal influence. Stress anisotropy is also examined, revealing that high-stress anisotropy allows for closely spaced fractures, theoretically eliminating minimum spacing concerns. Numerical simulations confirm that higher anisotropic stiffness reduces fracture width in TIV formations, aligning well with analytical modeling results. Wide fracture spacing produces a uniform and symmetric geometry, while narrow spacing results in non-uniform and asymmetric growth, with some fractures failing to initiate. Fracture turning due to stress anisotropy was also investigated, and the fracture propagation results clearly demonstrated the starting of fracture turning between 1000 to 1500 psi stress anisotropy. Micro-seismic was used to validate and calibrate fracture geometry. Economic-based optimization determines the optimal fracture spacing.
Engineering Innovation of Sustainable Ideation for supper Corrosive Environment CO2 - First Non-Portland Cement for Primary Barrier in Middle East and First Non-Portland Cement Application for CCS well in World.
Authors: AL Menhali Shamsa Jameel , Ibeziako Chuma , Ramadhan Taha Mohamed , Al Sabi Haya Jamal , Arbadov Gurbandurdy , Anwar Mohamed , Paun Bogdan , Ziganshin Ramil , Al Shamsi Eissa , Hamdy Ibrahim , Sumaida Ali Sulaiman Bin , Hamdan Tariq , Abdelazim Mohamed Farouk , Siqing DR. , El Youssef Bassem , Saputra Tarmiji Darma , Abuirshaid Shadi , Amer Ala’a , Alade Ayokunle , Tambe Romana , Permana Erik , Zulkifli Amul Husni , Khan Pattan Noor Ahamed , Escalante Jose , Sandra Dewi Novita , Aljadid Mohamed , Siddiqui Faisal , Qassem Mohamed
Published: February 2024

Abstract/Objective

The challenges associated with the construction of CO2 injection well for carbon capture sequestration (CCS) are cross disciplinary segments, but highly manageable. Starting from Site selection, reservoirs identification, drilling well, cement operation execution, geological formation evaluation and reservoir specialists that ensure detailed evaluations of the subterranean environment.

Improving sustainable Wellbore barrier CO2 Injection Wells required carful design cement slurry that can tailored withstand drilling risk (Mud Losses/Stuck/) and maximum storage in corrosive environments to enable carbon capture storage. Additional, work reviewed on enhancing properties Portland cement were by reducing permeability of cement sheath or lowering the concentration of materials in the cement matrix that react with CO2 or replacing the Portland cement with specialty cement to sustain long term CO2 well injection.

Effort have focused on sustainable technology to look further promote long term well seals to deploy non-Portland for the first time in casing with fiber optics installation along 9 5/8" intermediate section for better data acquisition system (DAS) across the aquifer, successful coring formation, mini frac operation and cement across aquifer with low fracture gradient tolerance in UAE.

This paper discusses a novel first application new sustainable solution of a cement selection in a supper corrosive environment CO2 injection well in Middle East general and UAE specially. Additionally, reviews the first CCS appraisal well in Syncline area targeting the deep saline aquifer formation CO2. This appraisal well is aimed to become an enabler for developing a saline aquifer CCS business/Commercial model. On the other side, the well is designed to provide operational learning and de-risking for potential future large scale CCS project and will be kept as part of a future large scale CCS project where possible.
Application of Gas While Drilling in Fluid Detection and Perforation Strategy in Jurassic Reservoirs
Authors: Nguyen Kim Long , Shehabeldin Mahmoud , Al-Sabea Salem Hamad , Moustafa Ahmed , Ousdidene Karim , Sharma Sachin , El-Masry Mohab
Published: February 2024

Nowadays, the acquisition of Gas While Drilling (GWD) is applied widely in the Oil and Gas industry. The analysis of gas data plays a remarkable role in evaluating the hydrocarbon potential of the formations, especially in high-pressure, tight, and fractured carbonate reservoirs. In addition, the GWD finds its application in geo-steering to drive high-angle or horizontal wells to achieve maximized reservoir contact (MRC). This paper presents a procedure of interpreting gas data while drilling and its application in formation evaluation, determination of productive intervals, fluid characterization, and identification of fluid contacts. The study discusses the Upper and Middle Jurassic reservoirs in New Fields, West Kuwait. The excellent result of the GWD analysis efficiently identifies the hydrocarbon potentials and fluid contacts in New Fields. The well test results of recently drilled wells absolutely prove the successful deployment of the GWD method in West Kuwait.
Innovative Emissions Monitoring and Management Methods to Reduce Environmental Impact During Well Testing Operations
Authors: Franklin G. C. , Finley D. , Fink K. , Kool H. , McGouldrick M.
Published: February 2024

Well testing is crucial within the oil and gas industry. When production facilities are unavailable, hydrocarbon can flare during the gaseous or liquid phase. When this occurs, smoke and fallout from the burner are the most visible byproduct; during which, hazardous CO, CO2, and SO2 can be emitted. The choices available to the operator to manage produced hydrocarbon in an environmentally acceptable manner can be limited and driven by project economics.

Carbon production accounting is rapidly growing as a decision-making process for operators. Within just the last few years, operators have rapidly increased their commitment to reduced carbon emissions. However, proper quantification and monitoring of the environmental impact, particularly during well testing activities, still relies on dated gross calculations performed even before the operation. Innovative technologies and software are discussed to help control and evaluate the impact of emissions in real-time.

Current options available to operators are discussed with respect to management of well testing emissions and the trade-offs that must be considered during selection of the optimum well test design. Various case examples are highlighted, showing how technologically advanced solutions (i.e., hardware and software tools for the continuous acquisition, evaluation, monitoring, control, and reporting of emissions data) can provide critical information that enables environmentally acceptable decision-making in real time to support emissions abatement. Furthermore, the operator should examine all options available to design a well testing program with minimum impact to the environment. These include, but are not limited to, emissions management techniques at the surface and downhole as well as innovative operational efficiencies.
Machine Learning-Assisted Petrophysical Rock Type Classification and Permeability Estimation with Flow Zone Indicators
Authors: Andrew EbunOluwa , Xu Chicheng , Odi Uchenna , Sheludko Stanislav , Silver Andrew , Zayer Yaser
Published: February 2024

The main objective of this study is to derive petrophysical characteristics and predict petrophysical rock types (PRT) from a well log from the Volve Field Dataset, using unsupervised and supervised machine learning algorithms. We utilized available core data of the reservoir with an unsupervised K-means algorithm and supervised random forest classifier to predict rock types and permeability of the well. This paper proposes a methodology that takes advantage of calculated flow zone indicators (FZI) and hydraulic units for enhanced characterization.

The core data set containing 557 core plug samples, which consists of porosity, permeability, grain density, and water saturation measurements, was classified into PRTs based on core FZI and hydraulic units (HU). These calculated FZI and HU values of the core data acted as the training data set to estimate HUs for the well log using random forest classification. Permeability was then estimated from a linear regression associated with each HU, based on the calibration to calculated core FZI values.

This paper documents the ability to conduct petrophysical characterization with minimal initial core data. By applying the K-means algorithm to intuitively identify sedimentary and diagenetic characteristics of the reservoir, and by applying the random forest algorithm on a conventional parameter such as FZI, one can sufficiently estimate petrophysical properties and classify PRTs from well logs in a time-saving manner.
Artificial-Intelligence-Based Drilling Risk Prediction - Prevention is Better than Cure
Authors: Hussain Syed Aaquib , Hasija Ketan , Guillot Valerian , Sesboue Pierre , el Ganaini Ahmed , Hughes Todd
Published: February 2024

Efficient drilling operations necessitate accurate risk assessment to prevent wellbore failure and consequent nonproductive time (NPT). In this work, the authors present a comprehensive strategy to enhance subsurface well planning decisions using artificial intelligence (AI) and machine learning techniques, optimizing the estimation of integrated drilling contracts value, specifically Authorization for Expenditure (AFE) models.

In this study, authors address the challenge of quantitative risk assessment during drilling operations, aiming to predict minor impacts that can lead to catastrophic wellbore failure. The primary objective is to develop an AI-driven interactive dashboard that assimilates historical drilling experience to provide superior decision-making for well planning.

The approach involves analyzing historical offset well drilling events to identify critical risks leading to NPT. By leveraging the Gower similarity (GS) algorithm, a similarity matrix is established between observations from offset wells and a new drilling contract. Thirteen key features, including section depths, expected rate of penetration (ROP), mud characteristics, mud density, and dog leg severity (DLS), influence the clustering process. The resulting similarity matrix informs an unsupervised hierarchical clustering algorithm, optimized through Silhouette analysis. Subsequently, Monte-Carlo simulation is executed on derived risk categories to yield more precise AFE estimates.

This novel approach is validated using a diverse offset well database from 20 countries, with a specific application in a Middle Eastern country. Analyzing data from more than 252 historically drilled wells across five fields, the study predicts drilling risk categories and associated NPT for a targeted well. Visualizations, including interactive charts and maps, illustrate the distribution of risk categories among offset wells. Post-outlier removal, risk categories from 139 offset wells are selected for Monte-Carlo simulations. Predictions are presented in terms of occurrence probabilities and total NPT, promoting a more informed AFE. Collaboration with drilling domain experts and blind tests further corroborate the approaches effectiveness.

In this study, the authors pioneer a real-time monitoring methodology for drilling events and risks, harnessing evolving machine learning and AI advancements. By leveraging historical data and expert insights, it successfully improves the cost-effectiveness and safety of drilling practices. The approach stands as a testament to the power of AI in revolutionizing drilling operations.
A Quantitative Approach to Characterizing Natural Fractures in Tight Gas Sands, Using Data from Mud Loss
Authors: Tan X. , Li H. , Wen L. , Liu H. , Liu T. , Jia C. , Wang S. , Zheng R. , Liu J.
Published: February 2024

Natural fractures have a significant impact on the development of tight sandstone gas reservoirs. However, over the years, limited types of data have been used in characterizing fractures, leading to generally unsatisfactory prediction accuracy. This paper presents a quantitative prediction approach using data from mud loss during drilling, taking the J Gas Field in the Ordos Basin, China, as a case study. The target zone is the H1 member of the Upper Paleozoic, considered a set of superimposed braided-river sand bodies. In this geological setting, horizontal wells have been the preferred choice for exploiting Zone H1. The approach comprises the following steps: First, fracture development characteristics were obtained by studying core samples, image logs, lithology, bed thickness, and seismic attributes. Second, the importance of data from mud loss on fracture prediction was discussed, and the relationship between fractures and faults was analyzed. Third, log responses after core calibration were collected at mud loss points, and potential fractures were identified through well logging. Finally, a 3D model of fracture intensity was created to provide a quantitative characterization of fracture distribution, and the prediction accuracy was verified. The key findings are as follows: (1) Mud loss data can help in locating subsurface clusters of fractures, as mud loss is inherently linked to fracture dimensions. Therefore, mud loss can serve as a valuable index for describing fractures. (2) Major faults play a crucial role in controlling the distribution of fractures, with the orientation of faults also influencing this distribution. For instance, East-West faults have a more pronounced impact. (3) The occurrence of mud loss has a dual nature. On one hand, moderate mud loss can be an indicator of promising gas production. On the other hand, uncontrolled mud loss can result in severe formation damage and unexpected drilling termination. (4) The constructed fracture intensity model offers a quantitative prediction of fracture distribution, and the result demonstrates a satisfactory prediction accuracy. What sets this paper apart is the introduction of a quantitative methodology for predicting natural fractures, making full use of often neglected mud loss data.
Effective Options Utilized to Reduce or Eliminate Non-Productive Time in Case of Severe and Total Losses While Drilling Unconventional Horizontal Wells
Authors: Almasmoom Salahaldeen S. , Mugdad Ahmad I. , Yin Guoqiang , Qahtani Faris A.
Published: February 2024

The horizontal wells in an unconventional area of interest are designed with a combined 8-½-in vertical/curve/lateral sections. The section is typically drilled with oil-based mud (OBM). Severe to total losses might occur while drilling through (or bellow) potential loss zone (depleted zone) in the vertical section. When losses happen, drilling further to the well total depth (TD) stops. Conventional ways to cure the losses are time-consuming. By applying the presented practices in this paper, the losses are successfully cured in most cases to allow drilling ahead to well TD. The results are less overall risk, less non-productive time (NPT), and more cost savings when compared to the traditional methods.

Typical applied traditional methods to cure the severe/total losses are tripping out of the wellbore to change the bottom hole assembly to allow pumping higher loss-circulation materials (LCM) pills, setting a cement retainer inside the previous 9-⅝-in casing, and squeezing cement below the retainer to the loss zone. When the cement retainer is run, an additional cleanout BHA is needed to drill out the cement retainer and cement below it to continue to drill the remaining section. The worst-case scenario when the losses are not cured is running and cementing a 7-in liner to cover the loss zone. This option is very expensive and time-consuming.

The team applied new strategies to reduce the NPT required to cure the losses, and to attempt to eliminate the losses from even happening while drilling through the potential loss zone. The NPT reduced dramatically when compared with wells drilled using the conventional methods, and it is eliminated in most of the newly drilled wells. The drilling fluid system (also called mud system) is modified to reduce the associated cost of the fluid lost to the loss zone. Furthermore, circulating sub is used in the bottomhole assembly (BHA) to allow pumping high concentration LCM pills without the need to trip out to change the BHA configuration. The developed pump-and-pull (P&P) cementing technique is used instead of the cement retainer when pumping cement is required. However, the most important practice is strengthening the potential loss zone while drilling through and below it to eliminate the NPT completely. A note to mention is that the loss zone in some areas in the unconventional area of interest is highly fractured to the point where even the developed methods are not successfull. Nevertheless, the success rate of these newly applied operational practices is high across the majority of the area of interest.