bluuebunny/crossref_metadata_2025_split huggingface dataset:
Horizontal Well Calibration Using Zone Log Matching and Machine Learning
Authors: Hasan Hesham , Parra Humberto , Gerami Arshia , Singh Manish Kumar , El Jundi Obeida , Stojic Slobodan , Jeddou Houcine Ben , Gacem Tarik , Kloucha Chakib Kada , Mustapha Hussein 
Published: October 2022

Abstract
Placing horizontal wells in the correct zones of the producing reservoir in static/dynamic models is important for robust model quality and essential for production history matching. A laborious technique of manually generating correction points around each un-calibrated well is often used by geomodelers. This technique is not systematic and is highly interpretive. We present an automated global solution that leverages not only the well tops, but also the well trajectory information to calibrate the horizontal wells.
The solution comprises a series of processes that can all be run through a custom built graphical user interface (GUI). The processes are designed to a) detect the calibration problems with the ability to visualize them, b) correct each surface separately for zone mismatch and c) retrieve original zone thickness whenever possible. Treating each surface separately simplifies the problem and causes great reduction in run time compared to simultaneous correction of all surfaces in one go. However, the final results are essentially global, which is insured by maintaining the input zone thicknesses (except if well data confirms otherwise).
The correction is achieved by ensuring that model zones (between the input surfaces) match the "optimum zone logs" at all wells in the model. The term optimum, in this context, means that the zone log should only contain the zones to be modeled, no more no less. These optimum zone logs are typically calculated from the well tops, which implies having an impeccable set of well tops.
In most cases, well tops sets may contain extra tops (that are not included in the model) and missing tops at some of the wells. While the latter problem is more difficult to solve, both problems must be detected and corrected for a successful run of this solution. Fortunately, the "diagnosis" step at the beginning of this solution detects all these problems, which reduces the time needed to review the well correlation, especially in fields with hundreds of wells. Moreover, machine learning techniques are used to suggest the position of the missing well tops. The user gets to either approve the suggestion or change it manually.
The solution we are presenting is fully automatic and fully reproducible. It is given with four parameters to control the amount and influence of the correction applied to the horizon intersecting the trajectory when it is not supposed to. If optimum zone logs are ready, the solution can run in few minutes to correct large models with hundreds of wells and tens of surfaces. This has the potential of reducing months of work to few days to calibrate horizontal wells in a structural model. The few days may include optimization of the solution parameters to achieve the desired results.
Internal Deformation of Layered Evaporites, Gotnia Formation, North Kuwait: Insights from Seismic Characterization and Sandbox Experiments
Authors: Pichot Thibaud , Doublet Stefan , Rao Narhari , Odreman Allan , Kidambi Vijaya , Al Rashedi Hamad , Al Mutairi Talal , Bansal Parshant , Ahmad Munir , Schueller Sylvie , Mengus Jean-Marie , Richard Pascal 
Published: October 2022

Abstract
The Late Jurassic Gotnia evaporites of Sabriyah (SA) and Raudhatain (RA) fields, North Kuwait, provide an example of Layered Evaporite Sequences (LESs) in which well-preserved internal geometries resulting from a complex polyphased tectonic deformation can be investigated. The Gotnia LESs represent a unique opportunity to study the mechanisms of internal deformation of rheologically contrasted layers of anhydrite (strong) and halite (weak) matrix present in the km-scale SA-RA structures.
Combining seismic data characterization and sandbox experiment approaches, we describe in detail the spatial variations of deformation and place them in a sequential evolution of regional and local stress regimes. Seismic mapping was focused on the thickest anhydrite layer of the Gotnia LESs (i.e., 1st anhydrite). As anhydrite has a strong lithological and acoustic impedance contrast with respect to halite, seismic mapping was carried out on an impedance cube and a derived seismic attribute (salt probability index), thus increasing the confidence level of the map. A series of sandbox experiments focused on the SA structure was carried out. The experiments were coupled to X-Ray CT scanner to enable 3D geometries to be reconstruct at different deformation stages
Three types of internal structures delineating structural domains were identified on seismic: boudins and imbricated thrusts were observed above major reactivated faults (SA and RA), while isolated thrusts were restricted to the terrasse between SA and RA. Seismic imagery clearly illustrated the structures at present day, but it did not account for the genesis of boudins, imbricated stringers and isolated thrusts, nor their spatial distribution, as well as the potential link existing between them. Sandbox experiments provided critical insights into the way internal geometries deformed through the successive tectonic phases. Sandbox experiments demonstrated the sequential evolution of internal geometries of competent sand layer (anhydrite layer equivalent): the phase of transtension triggered the dislocation of the stringer encased in the silicone. However, the development of boudins was restricted to the footwall block. During the subsequent transpressional phase, thrust imbricates developed where sand stringers were previously ruptured.
It is suggested that internal geometries driven by salt tectonic processes, may have played a significant role in the actual layering of the LESs and could contribute to observed changes in thickness and compartmentalization of intra-Gotnia reservoirs. This result could bring new insights into reservoir facies layering and pore pressure distribution in LESs.
Maximizing the Value of NMR Logging while Drilling, a Combination of Laboratory Measurements and Well Log Data concept.
Authors: Serry Amr Mohamed , Yousif Sharifa Yousif , Budebes Sultan Ahmed , Zakaria Hasan Mohamed , Efara Mohamed Saher 
Published: October 2022

Abstract
The application of segmented, smart completion strings is expanding to enhance the hydrocarbons production along extended horizontal drains that are drilled across heterogeneous carbonate reservoir units, Offshore Abu Dhabi. Conclusive formation evaluation answers are required to support the well completion design. Nuclear Magnetic Resonance well logging while drilling "NMR-WD" supports the operations efficiency, HSE and adds a wealth of input to the completion design in a time-efficient process. A quantitative matrix independent porosity and permeability are required. In this case, we present the value of laboratory NMR measurements to maximize the value of the log interpretation process, adding a conclusive picture of the pore size distribution and reservoir quality as critical inputs.
Representative core plug samples of the different major rock types have been selected along the Upper Jurassic Carbonate sequence, Offshore Abu Dhabi. The plugs have undergone Soxhlet cleaning, and routine core analysis measurements before Mercury injection capillary pressure measurements were carried out on plug trims too. Saturation of the plugs was conducted using synthetic formation brine and desaturation has been conducted over porous plate. A complete scanning of the pore size and pore throat distributions was made available and the transverse relaxation time T2 cutoff for the irreducible fluid was identified, then applied to the NMR-WD dataset.
The laboratory measurements could be used to reprocess the log data, bridge the gap between the Coates permeability computation and the offset core values based on the measured benchmark T2 cutoff values. The integration between the porous plate, MICP and NMR laboratory measurements is utilized to qualify the NMR-WD as a vital tool for the future similar formation evaluation operations across the same target reservoir.
NMR-WD log data interpretation is enforced with a representative pore size distribution. The quantitative matrix properties representation improves the well completion design.
Deep Learning Based Seismic Elastic Properties Inversion Guided by Rock Physics
Authors: Ahmad Fuad M Izzuljad , Jaya Makky Sandra , Abdrahman Sharif , Lew Chean Lin , Law May 
Published: October 2022

Submitted Abstract
The aim in characterizing reservoir on seismic is to be able to precisely assess rock and fluid properties from seismic data. Despite several approaches available, geoscientists still facing issue to accurately characterize the elastic & reservoir properties from seismic. This mainly because of the implementation of simplified linearized algorithms and assumptions which unable to fully address problem with non-linearity and non-uniqueness solution. Besides, the conventional approaches also typically take a very long turnaround time due to extensive computational works. Hence, implementation of deep learning-based seismic inversion with incorporation of a generalized rock physics knowledge for elastic properties prediction perhaps might be able to address the issues. The proposed approach consists of 4 main phases. The first phase is to develop rock physics understanding based on in-situ available reference well data. In the next phase, realistic synthetic dataset library at various pseudo well location is generated that mimic the actual subsurface reservoir condition and the corresponding seismic data from those pseudos well library is subsequently simulated. The third phase is to implement U-net deep learning network architecture with residual blocks of ResNet-18 for training, validation, and testing, utilizing synthetic data library generated in the previous phase. The fourth phase is to apply the pre-trained predictive network model on the actual seismic dataset as input for rock properties estimation. The robustness of the proposed approach is first evaluated based on synthetic dataset. Exceptionally good coefficient cross-correlation is obtained after evaluation on the predicted and true elastic properties; while results on the blind test dataset are slightly less compared to the training performance. This shows that the implemented model architecture with specially designed residual blocks can generalize quite well. The network model able to qualitatively distinguish the properties variation and capture the background trend of each elastic property. Besides, the proof-of-concept exercise also is successfully verified on a fluvial dominated field in Malay basin. Qualitatively, the predictive network model able to capture the background trend with some minor overfitting and identify the relative deviation of the elastic properties. The promising results are also comparable with results produced from commercial software, yet with faster turn-around time and more efficient workflow. In this work, a new approach of deep learning-based seismic inversion is introduced for elastic properties prediction which incorporates a generalized rock physics knowledge and a complex deep learning networks architecture of U-Net with residual block ResNet-18.
Study on the Influence of Natural Fracture Modeling on Hydraulic Fracture Propagation for Horizontal Wells in Unconventional Resource - A Case Study from China
Authors: Li Jie , Xu Hanbing , Xiong Qiyong , Yi Yonggang , Zhou Wei , Zhang Liwei , Chen Shengen , Lian Liming , Zeng Meiting , Yang Xiao , Huang Xingning 
Published: October 2022

Abstract
Low-permeability oil reservoirs generally have the low production, production rapid productivity decline, and low-ultimate recovery. Staged fracturing is usually conducted for horizontal wells. Hydraulic fracturing technology is generally used to improve fracture conductivity and well productivity, which is one of the key technologies for Enhance Oil Recovery (EOR). How to accurately characterize and simulate the distribution characteristics of hydraulic fracture network in 3D space is particularly important. The stress interference between hydraulic fractures as the objective mechanical behavior in the process of staged fracturing affects the geometry fracture network and the productivity of the reservoir post-fracturing. The hydraulic fractures simulation in natural fractured reservoirs is complex shapes, mainly because natural fractures affect the propagation path of hydraulic fractures. The theoretical model used to describe the hydraulic fracturing in homogeneous reservoirs cannot accurately show the complexity of the spatial morphology of hydraulic fractures in naturally fractured formations. CNCP (the operator) urgently need a mechanical model that can show the stress interference behavior between multiple fractures and the direction of hydraulic fracture propagation, and be used to simulate the spatial form of multiple hydraulic fractures in staged fracturing of horizontal wells and their propagation behavior in naturally fractured formations. Aiming at the impact of natural fractures in the reservoir on the propagation path of hydraulic fractures, this paper established a mechanical model for distinguishing interference behavior of natural fractures and hydraulic fractures. And analyzed the stress field at the tip of the hydraulic fracture and the stress field acting on the natural fracture surface based on the theories related to rock mechanics and fracture mechanics. On the basis of coupling the 3D geomechanical model and the 3D Discrete Fracture Network (DFN) model, this paper established the discriminant model for hydraulic fractures penetrating natural fractures in 3D space to conduct hydraulic fracture propagation simulation for the horizontal well. The research results can be used to optimize the hydraulic fracturing treatment design, and provide technical support for the effective production and profitable development of low-permeability reservoir resources.
Monitoring Injected Magnetic Nanoparticles Movement with Distributed Magnetic Sensor
Authors: Hafizal Mad Zahir Mohamad , Zainal Suzalina , Lin Lew Chean , Zhafri Kamarul Bahrim Ridhwan 
Published: October 2022

Abstract
The hydrocarbon business employs nanoparticles for a number of purposes, most notably to increase oil and gas output (Sircar et.al., 2022). Nanoparticles, nanosensors, nanocomposites, coated nanoparticles, nanofluids, and many other forms of nanomaterials find usage in the petroleum industry. While this technique has found widespread application, the development of a robust approach for monitoring nanoparticles’ behavior at depth is still in its infancy. For waterflood IOR initiatives, where it is used as proactive tracer technology: 1) to distinguish injected fluid from background (Commer et. al., 2020), 2) to understand injected-water direction in reservoir (Eltoum et.al., 2021), fluid flood front, and updating/improving dynamic model, the use of nanoparticles combined with geophysical data monitoring will be a useful risk-mitigation strategy (Huang et.al., 2021).
Application of Logging While Drilling - Nuclear Magnetic Resonance in Reservoir Evaluation and Geosteering: A Case Study in Marrat Reservoir, Umm Roos Field, West Kuwait
Authors: Nguyen Kim Long , Fahmy Mahmoud Fawzy , Dzhaykiev Bekdaulet , Odiase Paul Osadebamwen , Al-Morakhi Rasha , Al-Ajmi Mohammed Mubarak , Verma Naveen Kumar , Quttainah Riyad 
Published: October 2022

Abstract
The Jurassic Marrat reservoir in Umm Roos field of West Kuwait (WK) is a low-permeability carbonate reservoir with heterogeneous petrophysical character that limited the predictability of reservoir properties and highly deviated development wells are preferred to maximize the exposure of the reservoir. However, there is an environmental risk and high operational cost when running radioactive-based porosity logging tools in such complex well profiles. To avoid that risk, Nuclear Magnetic Resonance (NMR) with conventional resistivity and gamma ray in a logging while drilling (LWD) program is proposed to achieve real-time formation evaluation and efficiently support well placement to maximize the reservoir contact. This paper presents the application of LWD-NMR as an alternative solution to evaluate reservoir properties as well as to land the highly deviated well successfully in the best reservoir sweet spots to achieve the maximum outcome for production.
Best Practices and Lessons Learnt from a Comparative Study of LWD Formation Pressure Measurement in Static and Circulating Wellbore Conditions in ERD Wells
Authors: Javid Khalid , Sainz Alvaro , Bascialla Guido Carlos , Alkendi Mohamed Ali , Al Shehhi Hamad Rashed , Al Hammadi Amina , Meherbi Salama , Telang Viraj Nitin , Majid Mohammed Abdul 
Published: October 2022

Abstract
Pressure measurements in low permeability reservoirs are generally supercharged and cannot be used for mud weight optimization, selection and placement of in-flow control devices (ICD) in lower completion design and future reservoir management. The paper focusses on analyzing the effects of static and circulating wellbore conditions on the formation pressure data acquired using logging while drilling (LWD) tools in extended reach drilling (ERD) wells.
Supercharging is commonly observed in low permeability zones mainly due to poor quality mud-cakes having low thickness and high permeability. Thus, pressure measurements using LWD tools read erroneous values almost equivalent to the annular circulating pressure. To reduce the effective overbalance and emulate wellbore environment for wireline logging, pressure measurements were performed in static conditions using LWD tools powered by batteries instead of downhole turbines. This paper discusses formation pressure datasets acquired using LWD tools in eight wells and thereby recommends best practices from the lessons learnt through the pressure testing campaign.
By reducing the effective overbalance from circulation (ECD) to the static mud weight, the effect of supercharging on formation pressure measurements was reduced. However, in lower mobility ranges, less than 5mD/cP, the supercharging effects were prominent in static wellbore conditions as well. In such cases, using a sampling tool to pump out and relieve the supercharged pressure in the near-wellbore invaded zone is the best method to acquire accurate and reliable formation pressure data in such low mobility zones.
ERD wells require formation pressure measurements done using LWD tools to mitigate pipe sticking risks and acquire formation evaluation data in a single pass resulting in saving rig time. While deploying batteries to acquire accurate formation pressure data in static wellbore conditions in every operation is a robust solution, it should be weighed against the formation testing objectives, borehole stability and differential sticking risks to avoid further down time in resolving these issues.
Exploration History and the Application of New Technologies Contributes to the Discovery of New Reserves in a Marginal Field in a Mature Basin, Abu Dhabi
Authors: Yamanaka Motoyoshi , Al Shamsi Jawaher , Aitken John 
Published: October 2022

Abstract
In 2021 INPEX/JODCO, successfully produced oil, gas, and condensate through drill string tests (DSTs) from Well-L located in Onshore Block 4, Abu Dhabi (INPEX, 2021). Two major factors contributed to the discovery: (1) application of new technological advances to exploration; (2) analyzing and integrating past geological interpretations. Onshore Block 4 is near existing oil and gas production infrastructure, which raised expectations for early-stage development and reduced time to market for any potential discoveries. Given the successful exploration results, further analyses of data collected from the well logs, core, and produced hydrocarbons will support continued exploration activities and play de-risking. Historically, exploration in Abu Dhabi has focused on large closure present-day anticline trends. Over time, new oil and gas discoveries have become scarcer and the discovered volumes smaller. One strategy to open new plays and unlock larger possible reserves is focused on drilling low-relief structures.
Well A was drilled in 1956 in the Onshore Block 4 area. Following this 1st well, 6 wells were drilled up until the mid-1970's. These wells were located using early stage 2D seismic and geological surface investigations. In the mid-1970's, due to the development of 2D seismic, it was possible to extract the top structure and a further 5 wells were drilled from the mid-1970's to 1990's. Most of the Onshore Block 4 area is located within a transition zone between sea and land, therefore, it was difficult to detect the top of the structure, even with 2D seismic.
In 2017, mega-3D seismic was acquired across Abu Dhabi by ADNOC. Based on interpretation of the newly acquired 3D seismic, certain anticline axis trends were identified that were drilled by deviated wells because of surface limitations.
Generally, if the source rock potential is high in the center of the intra shelf basin in Abu Dhabi, it is possible that hydrocarbons are charged to any porous zone. In addition, past geologists identified and drilled the major anticlines with limited information. It is important to know the related exploration history and apply new technologies to existing knowledge to find new reserves. Dry Hole analysis is a crucial evaluation method to understand the thoughts and rationale of previous geoscientists and used to augment the exploration and de-risking of various plays. Based on using this dry hole analysis, in 2021 oil, gas and condensate successfully flowed from an exploration well.