bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integrated Charge Assessment in Nashpa Block Area of Kohat Fold and Thrust Belt of Pakistan
Authors: Bartha A. , Ali M. , Siddiqui F. H. , Bhatti A. I. , Joppen T. , Nawaz M. A. , Akhtar S. , Khan I. 
Published: February 2024

The main goal of the 2D basin and petroleum systems modeling (BPSM) study was to understand the petroleum systems elements and processes in Nashpa Block Area of Kohat Fold and Thrust Belt (FTB) of Pakistan, including generation potential of presumed source rocks, timing relationship between hydrocarbon charge and trap formation, role of the faults in hydrocarbon migration and accumulation, uncertainties related to input parameters, and last but not least to support the risk matrix concept to increase the confidence in assigned risk values.
The area of interest (AOI) of the 2D BPSM study is located west of the Indus River in the Kohat Basin which together with Potwar Basin are considered as part of Sub-Himalayan Tectonic Province demarcated by the Main Frontal Thrust (MFT) to the south and the Main Boundary Thrust to the North (MBT). The Main Boundary Thrust displaced the Mesozoic rocks over Eocene to Miocene rocks in the footwall. This major compression created the Kohat and Potwar foreland basins defined by thin-skinned tectonics of thrust and duplexes (Wandrey et al. 2004).
In the area with very complex structural movements the potential traps located on regional antiforms are the consequences of the compressive deformation triggered by the collision of the Indian plate into the Eurasian plate. In addition to the general north-south shortening, additional displacement events took place as well, which significantly enhanced the properties of the Mesozoic-Paleocene reservoir rocks through fractures (Wandrey et al. 2004).
Drilling The Longest Reentry Well in Khafji Joint Operations with Smart Fit for Basin Drilling Services
Authors: Al-Thaqafi S. H. , Al-Jassam E. H. , AlDosari M. , Muheeb M. R. , Osman A. M. , Al-Majed A. , Al-Dossary M. 
Published: February 2024

Abstract
To boost the target production and to achieve the Kingdom of Saudi Arabia and the State of Kuwait visions, Khafji Joint Operations (KJO) started evaluating new projects to boost their daily production in 2020. KJO decided to drill an exploratory reentry well to confirm the existence of oil beyond the known area in one of its carbonate formations. This objective required drilling the longest reach reentry well in the field.
The well challenges were to sidetrack from a vertical existing well with high-collision risk and passing through shaly formations associated with wellbore stresses and instability. Also, other difficulties were drilling across zones with high risks of lost circulation and geological uncertainty. To effectively ensure reducing the drilling cost and flawless operations, an integrated solution from different domains including drilling, geomechanics, petrophysics, mud logging, well placement and drilling fluids were included in the project.
Drilling challenges have been reviewed from offset wells and previous drilling performance was analyzed to deliver a comprehensive well risk assessment. The risk assessment dictated different measures to be taken in the planning phase. First, a geomechanics study was used to optimize the trajectory design and define trajectory limits for each formation. Second, engineering analysis included a finite-element drilling dynamics study to evaluate combining the rotary steerable system (RSS) bottomhole assembly (BHA) with logging while drilling (LWD) to predict the dynamics and dogleg capability. Third, recommended drilling practices and roadmap were provided with contingency plans for anticipated risks. Finally, different drilling, formation evaluation, and geosteering services were included in the well design requirements to provide challenging well profile, real-time data assurance and decision making on reservoir fluid characterization, reservoir description, and detailed fracture characterization, which will be critical for proper well placement and delivering well objectives.
The well was successfully drilled, meeting the required objectives and delivered the longest reentry lateral in Khafji offshore using the latest generation of RSS and full geosteering and petrophysical technologies which enabled a cost-effective well placement solution in a complex geological environment with high uncertainty. The well delivered the required reservoir evaluation objectives, which opens additional future opportunities with similar application in Khafji field.
An Analytical Solution to Pressure Build-Up for CO2 Injection into a Saline Aquifer: The Impact of Stress-Dependency of Permeability
Authors: Liu Hui-Hai , Han Yanhui , AlRuwaili Khalid M. , AlTammar Murtadha J. 
Published: February 2024

AbstractAquifer pressure build-up caused by CO2 injection has been an important issue for evaluating the safety of CO2 geological sequestration in saline aquifers. Although several analytical solutions to aquifer pressure evolution have been developed, an important factor, the stress dependency of the permeability, has been ignored in them. This study develops a new analytical solution by incorporating the dependency based on the commonly used quasi steady-state flow approximation. Following some previous studies, CO2 plume evolution is calculated from a principle that for a given CO2 injection rate, the plume evolves in such a way that the injection pressure at the injection well is in its minimum. The stress-dependent permeability is expressed with the two-part Hook's model. The analytical solution gives the thickness of CO2 plume and aquifer pressure as functions of time and location. It also provides bottom hole pressure of injection well as a function of time for a given CO2 injection rate. The impact of stress-dependency of permeability depends on several factors, including the aquifer stress sensitivity and pore pressure changes during the CO2 injection. The dependency generally reduces the pressure build-up for a given injection rate because the increase in pore pressure enhances aquifer permeability.
Enhancing CO2-H2S Storage Predictions in High H2S Fields: Improving Model Accuracy and Kinetic Rate Information
Authors: Zaidin M. F. , Amin S. M. , Azuddin F. J. , Razak A. A. , Mohsin N. , Pin Y. W. , Tewari R. D. 
Published: February 2024

Abstract
In the context of carbon capture and storage (CCS), the presence of impurities, such as Hydrogen Sulfide (H2S), in the injected Carbon Dioxide (CO2) stream poses a significant challenge (Bennion and Bachu, 2008). This challenge is particularly pronounced in carbonate reservoirs due to the potential precipitation of the minerals (Ahmad et al., 2023). Such precipitation can have detrimental effects on carbonate rock properties, including a reduction in rock porosity and permeability (Labus & Suchodolska, 2017; Clark et al., 2018). Ultimately, these changes can impact injectivity and storage capacity (Wang et al., 2012; (Zaidin et al., 2018), making it essential to comprehend the geochemical reactions involved, which are demonstrated as follows:
How Complex Lithology Schemes Affect Drilling Rate Prediction: Machine Learning Study
Authors: Gamal Hany , Omotunde Omokayode , Duarte Miguel , Mohamed Omar , Elkatatny Salaheldin 
Published: February 2024

Abstract
Drilling operations for oil and gas wells are considered one of the highest operating costs for the petroleum industry, and hence, implementing the new technology-based systems is highly required for cost reduction and efficient functionality performance for the drilling system. This paper introduces a machine learning study for the role of composite lithology schemes on drilling rate prediction as it is one of the common practical challenges of developing machine learning models for predicting drillability rate. A field case study introduces artificial neural networks (ANN) and extreme gradient boosted trees regressor (XGBoost) machine learning models to develop and validate a drilling rate prediction. The dataset incorporates surface rig sensor parameters such as weight on bit, drilling rotation speed, pumping rate and pressure, torque (as input parameters), and drilling rate of penetration (as the predicted output parameter). Confirmed ground truth data, including lithological characteristics and formation tops, complements the dataset. Two wells’ data contribute to model development, while blind unseen well data validates the models. The training dataset encompasses complex lithology formations sandstone, dolomite, anhydrite, limestone, and interbedded shale. The methodology follows a comprehensive workflow covering data preparation, filtering and cleaning, statistical analysis, feature engineering, model development, parameter optimization, and accuracy assessment through coefficient of correlation, average absolute percentage error, and root mean squared errors. The results showcase the high accuracy of the developed machine learning models (coefficient of correlation exceeding 0.99) during training and validation while the blind testing showed R of 0.83 and 0.89 for XGBoost and ANN respectively. The study explores the impact of complex lithology schemes on drillability rate prediction, employing data analytics and machine learning models. Real-time drillability rate prediction, facilitated by this model, serves as a technical guide for optimizing drilling parameters, enhancing performance, and achieving optimal mechanical-specific energy.
Digital Solution for Well Surveillance in Stacked Reservoirs with Near-Critical Fluid Systems
Authors: Mogensen K. , Mata C. , Samajpati S. , Cremades P. , Uribe J. E. L. , Al Zaabi M. 
Published: February 2024

Abstract
Fully compositional integrated asset models (FC-IAM) are being deployed for an increasing number of fields in the company's portfolio. Field A described in this work comprises four stacked reservoirs, each containing a near-critical fluid system with significant compositional depth gradient. Augmenting the FC-IAM with high-frequency sensor data as well as proprietary tools to actively monitor well performance helps identify and pursue opportunities to maximize the oil production rate from the field, subject to several system constraints.
Fluid properties were modelled with a cubic equation of state tuned to laboratory data to address some key challenges: Near-critical fluid systems giving rise to compositional variation versus depth.Injection of produced gas that develops multi-contact miscibility with the original reservoir fluids.Blending of fluids at surface from four stacked reservoirs.Operational requirement to maintain the bottom-hole pressure above saturation pressure.Validation of raw well test data before shrinkage correction (line conditions).
Compositional surface network models are run automatically on hourly basis and compared against real-time data. A surveillance algorithm identifies opportunities and assigns them to well owners. Activities are managed through a high-level tracking and value-capture system.
The asset team is consistently maintaining the well and surface network models assisted by the digital solution. The surveillance automation engine creates a feedback loop with the engineers which ensures that the models are of sufficient quality for production optimization. Models reproduce the actual oil and gas produced rates within the accepted accuracy range and are used routinely in optimization scenarios. The surface network model is also run in transient mode to study and optimize flow in two large subsea multiphase pipelines. Total value generation from implementation of the complete framework therefore exceeds expectation based on the steady-state production gain. The intangible value associated to reducing engineers’ workload, better data accessibility, HS&E and efficiency of operations has set a strong foundation for the strategy of the company to grow its enterprise value through increased volume and cost reduction.
Implementation of a compositional model framework is still uncommon as most integrated asset models rely on a black-oil formulation for the fluid property description. Near-critical fluid systems undergoing miscible gas injection add an additional layer of complexity in terms of modeling and surveillance efforts. Real-time data have proven indispensable for keeping the IAM up to date and for identifying opportunities for optimizing the production.
Maximizing Production and Cost Optimization in a Remote Carbonate Oilfield through a Standalone Waterflooding System: An Oman North Case Study
Authors: Al-Mamari A. A. , Al-Ajmi M. A. , Al-Omeiri H. N. 
Published: February 2024

Abstract
Oxy's Oman North concession, a remote carbonate oilfield with a gas cap, initially produced under natural depletion, but production declined rapidly due to low reservoir pressure and a weak edge aquifer.
To address this issue, waterflooding was deemed necessary. However, the field's location, which was approximately 50 km away from the main production station, posed a challenge because it would require considerable time and cost to install water injection facilities that are connected to the main station and provide the required produced water for injection. To overcome this challenge, a standalone system was designed with surface pumps for water injection. The project was executed within a year at a cost that was about 25% of constructing a water injection header from the station.
The system's design included horizontal injectors that were aligned to support horizontal oil producers and prevent the encroachment of the gas cap further downdip. Injection optimization and redistribution of water among the injectors were strategies for maintaining high production and helped to control costs, as they ensured that no injected water or energy was wasted without a return of oil. For example, voidage replacement ratio and diagnostic plots were produced on a monthly basis to monitor the injection performance. The surface network was also optimized using Prosper and GAP modeling tools along with day-to-day monitoring. Wells were choked and opened to maximize production for the entire network. The implementation of these best practices resulted in a significant acceleration of field production. Within the first 18 months, production tripled, and then reached more than five times pre-waterflood rates by 2023.
This paper presents an innovative method within the industry that effectively increases production while being cost-efficient. It introduces a pioneering approach that revolutionized production processes, achieved remarkable results, and ensured significant growth in production capacity.
Breaking the Chain, Novel Use of Adaptive Model Approach for Reservoir Modelling Reduces Time Duration of Project and Improves Collaboration
Authors: Thompson A. R. , Bolivar C. E. Zerpa 
Published: February 2024

Abstract
This paper details how the reservoir modelling workflow can be accelerated, and uncertainty reduced even for challenging Green Field prospects by constructing multiple small fit for purpose integrated adaptive models instead of just one all-encompassing model.
Adaptive models are models built with a single purpose as compared to more conventional reservoir models which are designed to be flexible and be used for multiple purposes. By only having a single task, these adaptive models can be built more quickly and since they are used as tool, not the product, they do not need to be reviewed. Multiple different adaptive models are built to handle different questions ranging from production data QC, different facies models going through to local history matches. As the study progresses, these adaptive models can be combined to consider multiple components and their influence on each other to ensure compatibility and to possibly feed information to the modeling effort for the full field model. As the project finalizes, these adaptive models work like jigsaw pieces to come together to create the finalized picture/model.
In the application of adaptive models within an integrated reservoir study, the most obvious benefit is being able to complete the static and dynamic aspects of the model building in parallel and subsequently be able to move more quickly to the deployment of the model for placing wells. Another key benefit was that it was possible to evaluate and defend new concepts or methods in reservoir modeling. Integrating new components into an existing modeling doctrine is challenging as significant time has to be expended and this has to be justified in advance of knowing the impact of the new item. With adaptive models, it is possible to evaluate out of doctrine ideas in parallel and off critical path. If the results are positive, the adaptive model is available to justify the benefit of the change and to also act as a comparison to the result following the doctrine.
This innovative workflow enables more efficient working by reducing the number of items on the critical path while simultaneously improving integration by enabling static and dynamic data to be integrated before the static model is finalized. In addition, this approach is a driver for innovation as it enables new ideas to be evaluated more rapidly and without having to impact the critical path of the project.
An Innovative Model Preconditioning Approach for Fast and Reliable History Matching
Authors: Kayode B. O. , Stephen K. D. , Albeshri R. 
Published: February 2024

Abstract
History matching is the process of calibrating a geological model to match the historical production, pressure and other reservoir characterization data. The methods developed in this work aims to address the history matching problem through an a priori integration of data-derived features into the static modeling phase, using a process we have termed pre-conditioned modelling. The methodology combines data mining techniques with intelligent visualizations to swiftly extract features from historical pressure and core permeability data, and integrate these features a priori into the static model. An anomaly detection algorithm was developed and used to detect data outliers and exclude anomalies from pressure data. A pattern recognition algorithm was developed, used for pressure cluster differentiation, and then to create reservoir regions containing wells within similar time-lapse pressure cluster, and the created regions were used as containers to guide permeability modeling and for applying sub-global permeability updates during history matching. The reservoir's internal architecture was derived from core permeability using a data mining algorithm we developed and termed fixed-window averaging. The intra-reservoir architecture was used to control the static model's vertical heterogeneity trends. This innovative approach to rapidly integrate historical pressure and core permeability dataset features into the static modeling process, resulted in a model that required no significant need for the traditional history-match process because the do-nothing match was excellent. A data analytics algorithm termed spatial Pythagorean search was developed and used to detect the causes of well productivity problems. An advanced visualization interface was developed which allows both time-based (e.g., water-cut, datum pressure) and depth-based (e.g., FTS, PNL, mobility, PTA) parameters to be displayed, permitting fully integrated decision making regarding necessary modifications. Leveraging holistic data visualization and data mining techniques, our methodology helps establish a reliable reservoir model by prior integration of data-derived features into the static model, significantly reducing the time required for history matching.
Wellbore Strengthening Uncertainties and Workflow Optimizations
Authors: Samy A. 
Published: February 2024

Abstract
Drilling depleted reservoirs is a challenging operation due to significant mud losses and potential risk of wellbore collapse against shale or high-pressure formations if mud weight has been decreased to mitigate mud losses across depleted reservoirs. Wellbore strengthening is an efficient and reliable technique to enable a continuous drilling operation for formations with different pore pressures such as drilling depleted reservoirs with the high mud weight required to support shale or high-pressure reservoirs.
If wellbore strengthening has been designed correctly and successfully executed, it could eliminate the total number of casing stages required to drill formations with different pore pressures separately to avoid mud losses or wellbore collapse during drilling. Decreasing casing stages will ensure reaching the target reservoir with larger casing size, thus improving well productivity.
Wellbore strengthening technique includes the initiation of short fractures where bridging particles are used to seal and hold the fractures open and prevent any further propagation inside formation. The hoop stress around wellbore will be increased accordingly and enable drilling such depleted reservoirs with higher mud weights. This paper discusses the methodology and assumptions considered in wellbore strengthening design including fracture propagation and the optimum geometry required to yield maximum wellbore strengthening during drilling. The paper will also propose an optimized workflow to be used as guideline for this application.
Wellbore strengthening design includes two main parts: fracture width opening and particle size calculations. There are many different analytical methods which can be used to calculate fracture width including PKN, GDK, etc. This paper presents a case study where all methods were screened and compared to the results of a pseudo-3D fracture propagation model. The most appropriate and reliable analytical method to calculate fracture width distribution inside formation during wellbore strengthening has been identified for complex geomechanical environment such as stress anisotropy field.
Fracture half-length is usually assumed in wellbore strengthening design to allow the calculations of fracture width. This assumption is not always right, it is associated with high degree of uncertainties and may not represent the optimum fracture geometry. This paper discusses a new design approach where fracture geometry can be optimized including the optimum ratio between invaded and non-invaded zones and the optimum fracture geometry required to maximize wellbore strengthening during drilling depleted reservoirs. A robust, reliable, and optimized workflow is also suggested in this paper to ensure that all uncertainties associated with wellbore strengthening design have been considered and addressed.