bluuebunny/crossref_metadata_2025_split huggingface dataset:
Surface Microseismic Monitoring for Over 30 Wells in Changning Region Sichuan Shale Gas Play, China: The Discoveries
Authors: He L. , Chen Y , Wen R. , Fan Q. , Heng D. , Huang Z. , Wang B. , Abdel-Halim Amro 
Published: February 2024

Abstract
The surface microseismic monitoring technology has been utilized in Changning region Sichuan shale gas play for over 30 wells since the beginning of 2018. The microseismic events located in real time could help to optimize the pump schedule, estimate the diversion, and mitigate engineering risks. The wide coverage of the geophone channels allows for moment tensor inversion (MTI) which reveals the source mechanism, and so to identify the natural fractures more accurately.
The property of the microseismic events were integrated with the pump data, the completion design, the reservoir properties, the ant tracking results, as well as the production. The purpose was to find the optimum completion and fracturing design parameters, and more importantly to figure out which factors contribute the most to production. The MTI result provided more accuracy on identifying the natural fractures which are key for hydrocarbon flow but at the same time also bring challenges for hydraulic fracturing operation.
The surface microseismic monitoring technique provides decent results for reservoirs with TVD range of 2100-3850m in Changning region. The number and magnitude of microseismic events is dependent on natural fractures. The higher pump rate tends to generate fracture network with bigger width and so higher complexity and to achieve higher estimated stimulated volume (ESV). The MTI properties could help to identify natural fractures more accurately and calibrate the ant tracking results. The relative angle and distance of natural fractures to the well trajectory influences the ESV and so the production. The ESV of a single stage barely shows relevance to the production log, but the ESV of a well does illustrate relationship with production of the well, especially the accumulated production of 3 months.
The rich data of surface microseismic monitoring in over 30 wells in Changning region leads to better integration for reservoir properties, completion and fracturing design, microseismic properties, natural fractures as well as the production.
Accelerating Drilling Efficiency and Consistency: A Rig-Based Approach to Exponential Drilling Program Improvements
Authors: Major C. , Hall T. , Stogner W. , Kern S. 
Published: February 2024

Abstract
The evolution of the drilling process has been sustained by consistent innovation and development, bringing the industry to where it is today. Innovation moves us forward: data is processed faster, flat time is reduced, roles are redefined; but the vehicle to achieve a successful drilling program remains: the drilling rig. This paper investigates efficiency in its many forms, and the foundational role that the drilling rig plays in fulfilling the global need for energy.
Over time it has become evident in the industry that evolving the rig design process helps to achieve greater overall success of a well program. As less flat time occurs, well cycle time decreases, increasing the total amount of feet drilled per day and likewise increased hydrocarbon production in that same timeframe. Innovative advancements, through progressive iterations in rig design, have led to significant efficiencies in flat time reduction through initiatives like rig floor automation and installing a rig walking system. As these efficiencies become more realized, consistency is increased allowing for better forecasting of long-term well programs.
A historical account of pivotal drilling rig advances will be discussed in detail, along with specific applications of automation and a walking system that modern well programs require. Creating a new rig fleet using repurposed equipment is not only economical, but also a sustainable solution and of significant value as carbon emission reduction increases in importance across operator and supplier initiatives.
Introducing walking rigs to a global fleet has contributed to reducing well cycle times by over 35% from 2016 to 2022. What was once a three-to-five-hour release-to-spud skid, is now as low as an hour and a half through the use of walking technology.
The benefits to a standardized rig fleet will be presented: reducing inconsistency, increasing quality of support, and enabling an agile support staff; while providing a consistent platform to launch drilling applications where newly learned best practices can be immediately applied fleet wide.
Today's drilling environment requires an integrated approach, enabled by both people and technology. The drilling rig provides a strong foundation, as a platform for further technological innovation through automation. As the industry continues to evolve, the clear benefit of field-proven equipment and technology supports breaking through barriers in unconventional drilling.
Utilizing Integrated Network Model to Optimize Production and Prevent Halite Formation in Pipeline Offshore Gulf of Suez
Authors: Sayed M. El , Korish M. , Mousa H. , Moussa A. 
Published: February 2024

Abstract
Scale deposition has always been an irritating problem for oil and gas industry. Scale typically results from a chemical state of supersaturation, which results from changes in pressure, Temperature & PH across production system. At this time precipitation reaction takes place and the mass of scale deposition is a function of the available amount of cation and anion species related to subject scale. Typical forms of scale are calcium carbonate & Sulfate, Strontium & Barium Sulfate. Commonly Deposition rate for such types of scales is quite slow due to limited concentration of relevant species. For the case of halite deposition chlorides salts are abundant species in water that in case of deposition would result in huge mass that may block tubing, flow lines and pipelines in short time.
Subject Field is located offshore Gulf of Suez, rimary reservoir is Nubian sandstone at depth of more than 11000 Ft & Temperature of 330 F. Formation Salinity around 350,000 PPM. Typically, water is in equilibrium at reservoir conditions and high productivity of wells allows high wellhead temperature which keeps chloride salts soluble in water.
The field produces through two 12" pipelines to a central processing facility. s the flow goes heat exchange takes place and soon balances with seabed temperature. Scale tendency calculations showed Halite would precipitate at 90 F. which means the vast majority of pipeline will be subject to the risk of halite deposition. This matches with a biweekly to monthly increase in line pressure impacting production due back pressure on producers and at critical point emergency shutdown would trip to protect line integrity.
The problem has been persistent that shutdown was expected on monthly basis and logistics consumed to pump fresh water followed by soaking time and pigging to ensure line pressure kept to minimum otherwise it will not take long before line plugs again. In 2020 a detailed Network Model coupled with scale formation prediction tool to determine pressure and temperature profile in subsea pipeline and expected scale formation rate with distance from platform.
Network optimization model showed that commingling specific wells on one line and keeping others on the other line would result in decreasing the risk of halite scale, investigations showed some incompatibilities between water from different wells that can be managed with scale inhibitor.
Application of the outcomes from network model in the field resulted in maximizing production eliminating downtime due to line pressure increase and eliminated the need for biweekly fresh water pumping.
Coupled Geomechanics and Reservoir Simulation for Gigantic Naturally Fractured Reservoirs
Authors: Qiao Lei , Shen Meng , Almani Tameem , Al-Shaalan Tareq , Dogru Ali 
Published: February 2024

Abstract
The objective of this work is to introduce the features of a new in-house geomechanics simulator. In this work, the capabilities of the new simulator will be demonstrated by applying it to coupled geomechanics and reservoir simulation models based on large-scale naturally fractured reservoirs.
The 3D geomechanics simulator presented here utilizes the finite element method (FEM) and was developed in C++. The geomechanical grid is created by extending the reservoir into burdens. The simulator is fully parallel using the message passing interface (MPI) library. The geomechanics simulator is developed as a standalone module and the data exchange (pressure, permeability) with reservoir simulator is performed using the multiple program multiple data (MPMD) paradigm. Faults and discrete fractures are modelled by ‘equivalent material’ constitutive relation, in which multi-fractures are included in a straightforward manner. Plasticity is modelled using an implicit closest point projection algorithm in the finite element context.
The in-house geomechanics simulator supports three modes: initialization only, one-way coupling, and two-way coupling. It supports poroelastic and poroplastic modeling for intact rock and discontinuities (faults and discrete fracture), e.g., transversely isotropic material (TIV) and Mohr-Coulomb plasticity. It allows users to specify burden grids and stress boundary conditions. It provides permeability correlations for intact rock and discontinuities, including classic correlations like Kozeny-Carman. Users can also define the permeability correlation through table input. The simulator outputs 3D stresses, strains and plastic strains, displacements, and faults displacements and strains. Results can be visualized using industry standard tools as well as open-source tools such as ParaView. Results of the coupled simulation have been verified against industry-standard simulators. The simulation of a billion-cell model has been performed to demonstrate the capability of the in-house simulator for studying gigantic field models.
This work introduces a new in-house geomechanics simulator that is coupled to an in-house reservoir simulator. It demonstrates the capability to simulate billion cell reservoir models with natural fractures. Use of the multiple program multiple data (MPMD) paradigm via MPI offers a significant improvement in performance and efficiency when compared to a traditional file-based approach.
Upside Potential Risk Assessment in Clastic Reservoirs by Integrating Advanced Seismic Attributes
Authors: Salazar-Buendia I. A. , Elouair Y. , El-Hassan W. M. , Dass S. 
Published: February 2024

This study shows the application of a comprehensive seismic interpretation and analysis to delineate the potential reservoir extension based on the integration of advanced seismic attributes. The results allowed to propose a delineation well based on the chance of success (COS) evaluation.
The reservoirs in the area of interest are rich in clastic sediments, deposited between fluvial and shallow marine environments. If we consider the burial depth, some of these target reservoirs may exhibit unusual high porosities. The overlaying stratigraphic formations are sequences of shallow water carbonates and evaporites.
The clastic sediments at the reservoir level are characterized by showing low acoustic impedance contrasts, and the shallower carbonate sequences usually trigger the generation of interbed seismic multiples during the seismic acquisition. Most of the time, the seismic image at the zone of interest is highly affected by the late arrivals of those interbed seismic multiples. Adequate seismic processing techniques have to be applied to eliminate multiples before moving forward with the seismic reservoir characterization task.
Maximizing Profitability Through Automated Production Optimization: A Game-Changer for Digital Oil Field Operations in Kuwait Integrated Digital Field (KwIDF)
Authors: Al-Rashidi H. , Ranjan K. , Lara R. , Muhsain B. , Singh S. , Tripathi D. 
Published: February 2024

Summary
The paper outlines a comprehensive workflow for calibrating well models and optimizing well production. An automated well-model management system ensures data accuracy and timeliness by fetching information from the production database. Simultaneously, the well-production optimization process identifies opportunities for improving field development and production operations by analyzing real-time data and applying optimization techniques. The system empowers engineers with data-driven decision-making tools and provides recommendations for optimizing well parameters. The integration of multiple data sources, automated processes, and data quality control ensures the reliability of results. This automated approach enhances the identification of valid optimization opportunities and facilitates well performance management, leading to significant oil production gains and informed decision-making within the field.
Automatic Kick Detection Using Artificial Intelligence
Authors: Sha Qihao , Ding Yan , Cui Meng , Cui Yi , Gao Reyu , Zhao Fei , Xing Shiwang , Lyu Congyue 
Published: February 2024

Abstract
Complications related to kick account for more than half of total well cost. Kick is unintended influx of formation fluids into the wellbore when the pressure found within the formation is higher than the mud hydrostatic pressure. If the event cannot be detected and controlled in time, they will lead to increased non-productive time (NPT) and costs. For example, the maximum killing pressure required will be more than two times higher if the kick is detected 15 min later. To kill a kick, actions like circulating the well with heavier/lighter mud will require the field engineers to stop drilling. Even worse, a kick can potentially result in a blow-out event, which can lead to loss of life and a company's reputation.
In recent years, people have made dramatic progress in kick detection. In general, they can be divided into physical-based models and data-driven approaches. For physical-based models, predicted values of the kick indicators are calculated and compared with the actual values. However, most of the physical-based models are unable to deal with complex situations, such as directional drilling and change of formation, temperature change, compressibility of fluids, etc. The pattern recognition approaches mainly consider the flow pattern. Moreover, these methods only recognize simple patterns, which might not be enough for real field application, where the data can be dirty and noisy. The models’ performance can also be severely impacted if the labels are unbalanced.
We propose to build a real-time alarm system for early kick detection using artificial intelligence and real-time drilling data. The goal is to use this automatic alarm system to predict kick events ahead of time and hence reduce NPT. First of all, the historical drilling data need to be integrated with daily drilling reports to recognize the kick events and routine daily operations. Second, calculating the possibility of kick from the drilling data, by applying our statistical method. Then, the preprocessed data is divided into training/development/test set. The Long Short-Term Memory Fully Convolutional Network (LSTM-FCN) need to be built, trained, tuned and validated with the drilling data.
We have already trained and applied the method in Chang Qing oilfield. It calculates the possibility of kick events and raise an alarm when the possibility surpasses a threshold. The accuracy for this field application is 90%.
A Novel Method for Accurate Measurement of Fluid Saturation in Shaly Sandstone During CO2 Sequestration
Authors: Gao Jidong , Yuan Bin , Zhang Wei , Zhang Hongbin 
Published: February 2024

Abstract
Accurate monitoring of fluid saturation is crucial for preventing fingering or leakage during CO2 sequestration. Due to the difficulty in testing the cementation factor of shaly sandstone, the evaluation of fluid saturation in such reservoirs by the Archie equation may be significantly biased. This study presents modifications to the Rhoades capillary bundle model and the Waxman-Smits parallel model for the clay conductivity phase and cementation exponent, based on the principle of the best conduction pathway. we use two different conductivity models and fluid transport coefficients in rock pores to modified the cementation coefficient. We compare different conductivity models and refine them by fitting them to on-site CO2 storage monitoring data. We analyze the impact of parameter variations on conductivity and saturation, and verify the accuracy of the equation. It is found that the relative error of the modified model is 10.76% compared with the conductivity of the dual-water model. Shaly sandstone reservoirs are characterized by clay-water expansion phenomena, with clay conductivity comprising the predominant fraction of total conductivity. The variation in water phase transmission and conductivity can be divided into two phases. In the first phase, conductivity experiences a rapid increase, while in the second phase, it rises gradually in a linear fashion. In shaly sandstone reservoirs, the relative error of the modified model is 5.44%. The enhanced accuracy in saturation calculations serves as a safeguard against measurement errors by on-site engineers.
Hydrogen Price Race in Australia
Authors: Escobar-Burnham Esther , Szemat-Vielma Waldemar 
Published: February 2024

AbstractIn December 2018, the Council of Australian Governments set a vision (Australia Hydrogen Strategy) for a clean, innovative, safe, and competitive hydrogen industry that benefits all Australians and will be a major global player by 2030. This study will summarize the current and forecast uses and volumes of hydrogen along with estimating the potential future range of hydrogen prices and prices’ key drivers across Australia seven territories.The seven hubs are the basis to understand the range in hydrogen prices combined with the technologies available to produce hydrogen across Australia. These seven hubs are planned as a springboard to large scale production, although hydrogen projects can be outside these hubs as well. The impact of the drivers and price assessment across the green and blue hydrogen technologies were studied. Hydrogen price ranges were calculated and are based on the levelized cost of hydrogen (LCOH) for the different electrolysis technologies as well as, steam methane reforming, black coal and lignite gasification with carbon capture and sequestration.The price is equivalent to a Free on Board (FOB) or at wellhead price. The model was built to replicate the Commonwealth Scientific and Industrial Research Organization (CSIRO) LCOH for the mentioned technologies, which was then used to model the price for green and blue hydrogen across the seven states and territories. Since the model is based on LCOH it does not incorporate additional costs such as transport or company overheads and market forces such as supply and demand. A comparison of the hydrogen price for various generation methods across the states and territories of Australia was generated and analyzed. The key observations from the results include: 1) there is neither a leading technology nor a leading territory for the low or high hydrogen price; 2) variations in energy commodity prices are directly linked to the hydrogen price and the main factor to consider with green hydrogen production; 3) decreases in costs suggest efficiencies in technology across the green and blue hydrogen industry are starting to be realized; 4) short-term volatility is expected as the industry develops but over the longer term the price should stabilize towards the lower end of the range.With Japan is a strong partner aiming to reach hydrogen cost of A$3/kg., Australia must reduce the hydrogen production cost as much as possible to keep its competitive advantage amongst competitors. This paper will describe the hydrogen price calculation process, the variables, and considerations for each of the seven Australian territories to become the top producer and exporter of the region.
Machine Learning Application on Seismic Diffraction Detection and Preservation for High Resolution Imaging
Authors: Bashir Y. , Khan M. , Mahgoub M. , Ali S. H. , Imran Q. S. , Imran C. , Karaman A. 
Published: February 2024

Abstract
Capturing small-scale features in complex subsurface geology, such as Carbonate, through seismic imaging poses challenges due to the influence of heterogeneous properties of objects in the subsurface on propagated waves. The first step in machine learning (ML) involves supplying a sufficient amount of data to ensure the learning algorithm is updated and matured. In the absence of multiple shapes of diffraction data, the accuracy of your prediction for machine learning (ML) may be compromised. ML may also fail to detect the pattern of diffraction in the data. Following the learning process, our machine focuses on the critical task of target detection. This involves comparing the target with the given data and searching for a specific signature. In this study, data is provided to the system in the form of images and features. The learning algorithm can process the input to make predictions about the target. The concept behind machine learning is to minimize the discrepancy between your prediction and the target value as much as feasible. The preservation of diffraction amplitude in laterally varying velocity conditions is influenced by certain factors. The technique of ML destruction is employed to separate diffraction data, as traditional filtering methods tend to blend diffraction amplitudes in cases where there are one or multiple diffractions present.