bluuebunny/crossref_metadata_2025_split huggingface dataset:
Study On In-Situ Stress Evolution and Infill Well Fractures Propagation in Unconventional Reservoir
Authors: Li Xiang , Zhou Wei , Xie Lei , Xie Dan , Xu Hanbing , Cao Qianqian , Li Junfei , Ding Yajie , Dong Jingya , Liu Yan , Xu Gang , Han Zijian , Hou Cao , Chen Liten , Li MingHong 
Published: October 2023

Abstract
Fluid transport and rock deformation are the core scientific issues in oil and gas development. The development of natural fractures in unconventional reservoirs, the variety of fluid flow mechanisms in the formation, and the non-homogeneity and anisotropy of rock mechanics parameters make the coupling of seepage and geomechanics in unconventional reservoirs exceptionally complex. During the production process, the reservoir around the wellbore generates different degrees of pressure drop, which disturbs the original ground stress in the pressure drop area, and the reservoir stress evolves with the extraction time, i.e., four-dimensional dynamic ground stress. Accurately predicting the reservoir's four-dimensional dynamic ground stress field is a prerequisite for the design of fracturing and repetitive fracturing of infill wells. Therefore, this paper systematically summarizes the numerical simulation methods of oil and gas reservoir seepage - geomechanical coupling and fracture extension of infill wells and discusses the progress of multi-field coupling simulation and the latest research results in depth. Currently, there are various models of oil and gas reservoir seepage - geomechanical coupling, which can be classified as full coupling, sequential coupling, one-way coupling, and proposed coupling according to the form of coupling solution. Due to the complex geological characteristics of the reservoir, the current four-dimensional stress evolution model has been improved based on the traditional model, which is mainly a continuous medium model and a discrete fracture model based on the full coupling method, as well as an iterative coupling model. The ground stress decreases with the decrease of pore pressure during the development process, and the stress direction will be deflected. Fractures can affect the reservoir stress distribution pattern and trend compared to continuous media. This evolution of the ground stress state will cause the fracture extension of the infill wells to deflect and produce the "Frac-hit" phenomenon and cause the "microseismic barrier" effect. The study of reservoir seepage - geomechanical coupling and fracture expansion in unconventional gas reservoir development is a multi-physical field, multi-dimensional, and multi-scale coupling problem. In this paper, it is suggested to study the integrated geological-engineering solution and carry out research on the mechanism of complex fracture expansion during repeated fracturing of horizontal wells and fracturing of infill wells under four-dimensional ground stress evolution conditions to provide theoretical support for the sustainable and efficient development of unconventional oil and gas reservoirs in China.
Re-Inventing Pressure Retained Core Acquisition for Advanced Reservoir Property Determination
Authors: Wunsch D , Rothenwänder T. , Anders Erik , Deutrich T. , Brown D. , Ramoin J. , Verret C. , Mukherjee P. 
Published: October 2023

Abstract
Innovation has always played a key role in past industry transition periods and helped to unlock the true potential of new technologies. For this reason, it is crucial to utilize and adapt these past experiences to effectively approach and tackle the challenges any operator is currently facing. The challenges range from understanding production behavior of reservoirs at any point of their lifecycle as well as CCS scenarios. Whenever injection is considered at any stage throughout secondary-, tertiary recovery stage or the general ‘re-utilization’ of the reservoir for storage respectively a thorough assessment is required. This increases the demand for sufficient data acquisition methods or workflows to overcome numerous shortcomings.
With full bore core data being one of the key elements for ground truthing any data set used for reservoir modelling and project decision making (Saucier et al. 2022), the conventional methods utilized to acquire these core samples have a variety of weaknesses. While these standard methods are well established, more advanced coring methods are required to provide more comprehensive datasets for reservoir description. The method discussed in this paper aims to address these demands by delivering a high-quality in-situ core sample which is then processed on-site and introduced to best-fit lab workflow.
Different special methods in the field of core acquisition are compared and strengths and weaknesses provide the context for potential need for a large diameter pressure coring technology. How this technology directly helps operators to better understand their reservoirs in any of the above-mentioned reservoir scenarios will be explained by describing different exemplary fields of application. These descriptions range from more accurate saturation determination of ROZs in depleted formations to acquiring in-situ PVT data for recombination of fluid volumes in conventional reservoirs to actual OGIP and GOR measurements in unconventional reservoirs. With the ongoing shift in the oil-&gas industry, pressure coring technology also has a high potential to become an important tool in storage efficiency assessments in CO2 injection wells for CCS applications.
The study outlines how pressure retained core samples can contribute to reduce uncertainties and improved datasets which are needed in cases where the design of reservoir models require comprehensive knowledge of the entire spectrum of reservoir data. The proposed best practices are backed up by findings from recent achievements as well description of field activities in different applications. The study aims for giving an overview on how pressure coring technology enhances the available toolbox for downhole data acquisition and how the technology brings added value to the industry in an environment when more stringent economics rely on more accurate data validation of any asset.
Different Origin of Microseismic Activity Highlighted by Magnitude of Completeness and B-value Computation Case Study on Underground Salt Exloitation and Storage
Authors: Fortier Eric 
Published: October 2023

Abstract
Seismic monitoring becomes an important and reliable method to identify instabilities induced by underground operations. Depending on the type of operations, as underground storage in salt caverns, hydraulic stimulation, or reservoir monitoring, different types of seismic activities are identified. After the location of micro-seismic events, different parameters are computed, like the peak ground velocities, the magnitudes, the source radius, or the B-value.
When a network does not have enough sensors to calculate reliable focal mechanisms, the use of such parameters can help us to better understand the seismic origins, their occurrence, and what induced the micro-earthquakes.
The Richter magnitude is an important attribute to characterize the seismic energy induced by cavern activities. The magnitude is computed from the seismic moment (Aki, 1966; Kanamori, 1977). The magnitude of completeness (Mc) is the minimum magnitude above which all earthquakes within a certain perimeter are reliably detected. The B-value computation corresponds to the slope of the number of located events versus their magnitude on a log scale. It gives an indication of the relation between the "earthquake" size and its occurrence. It is generally accepted that b-value around 1 is observed for "structural origin" and around 2 is observed for "small induced micro-seismicity".
Some sites are leaching or storing gas in underground caverns inside salt formation. Those sites are equipped of seismic networks using sensors in shallow buried arrays to monitor the associated microseismic activity. A significant seismicity is occurring since many years of monitoring. According to the B-Value computation, we manage to identify three types of seismicity. First one is cavern leaching seismicity associated to a B-value close to 2, indicating small energy activity located in the vicinity of the caverns. The second and third ones are gas storage caverns, and salt coverage seismic origin, with a B-value close to one, showing a more important seismicity in term of energy, induced by micro-structure re-activation or insoluble layers sliding.
This seismicity interpretation allows a new viewing of understanding the micro-earthquakes origin induced by underground challenges. Taking into account the geological context, we can better define the nature of the seismicity by computing such parameters. The only factor needed to compute a reliable magnitude of completeness and B-value, is the necessity of a significant database of seismic events.
Cloud Based Open API Applications Delivers Differential Value in Well Planning Efficiency Saving 60% of Well Planning Time
Authors: Mwansa Peter Levison 
Published: October 2023

Abstract
As part of ADNOC Onshore's aim to standardize and improve well planning efficiency and standardize, a Digital Well Technology Piloting Agreement (TPA) was signed to test cloud based open application programming interface (API) aware well planning software.
ADNOC Onshore engineers currently perform well planning and design in multiple legacy applications, requiring them to move the inputs and outputs from one place to another manually. For example, the well targets are identified by a different team in a service provider platform tool. The trajectory is then planned using a different vendor software. The rest of the well planning is completed with another legacy software, in individual modules for each aspect of design that don't integrate. Much of this is outsourced to "well services suppliers" to either do the full design or validate the internal design. The final execution document (Drilling Program) is completed in word and is typically 50-80 pages long. This is inefficient, and also allows for potential mistakes. If anything changes, all of this has to be re-worked. For example, if the pore pressure and fracture gradient (PPFG) analysis changes, there is no way to alert or notify the engineer that they need to update other parts of the well design (e.g. cementing, etc.). This creates communication challenges internally and with our third-party providers. It also leads to sub-optimal well design choices due to an inability to see the full picture.
Use of Critical Point in Equation of State Modeling for Reservoir Fluids
Authors: Pedersen K. S. , Christensen P. L. , Shaikh J. A. 
Published: October 2023

Abstract
Several equations of state have been proposed as a supplement or a replacement for cubic equations, but cubic equations have remained an industry standard in the petroleum industry. Cubic equations are bound to correctly predict the critical point of each component in an oil or gas mixture. With a minor correction, the location of the critical point also determines the simulated gas and liquid properties of a pure component at other pressures and temperatures. Also, for multicomponent reservoir fluids, the location of the critical point exerts a large influence on the phase behavior of the fluid in the entire gas and liquid region. The paper presents procedures that, for both reservoir oils and gas condensates, allow a reliable equation of state model to be generated with a fluid composition and an experimental saturation point as the only experimental data. This is possible by using an estimated critical point as an artificial data point. For oils, the estimated critical point is for the entire reservoir fluid, while for gas condensates it is the critical point of the C7+ fraction, since not all gas condensates have a critical point. The method can also be used to quality check existing EoS models or as an additional constraint when developing an EoS model by regression to experimental PVT data. This is particularly useful for heavily lumped EoS models.
Well On/Off Time Classification Using Recurrent Neural Networks and a Developed Transient Well Simulator
Authors: AlHammad Y. K. , Hoteit H. 
Published: October 2023

AbstractSupervised machine learning (ML) projects require data for model training, validation, and testing. However, the confidential nature of field and well production data often hinders the progress of ML projects. To address this issue, we developed a well simulator that generates realistic well production data based on physical, governing differential equations. The simulation models the reservoir, wellbore, flowline, and choke coupled using transient nodal analysis to solve for transient flow rate, pressure, and temperature as a function of variable choke opening over time in addition to a wide range of static parameters for each component. The simulator's output is then perturbed using the gauge transfer function to introduce systemic and random errors, creating a dataset for ML projects without the need for confidential production data.We then generated a simulated dataset to train a recurrent neural network (RNN) on the task of classifying well on/off times. This task typically requires a significant number of manhours to manually filter and verify data for hundreds or thousands of wells. Our RNN model achieves high accuracy in classifying the correct on/off labels, representing a promising step towards a fully-automated rate allocation process.Our simulator for well production data can be used for other ML projects, circumventing the need for confidential data, and enabling the study and development of different ML models to streamline and automate various oil and gas work processes. Overall, the success of our RNN model demonstrates the potential of ML to improve the operational efficiency of various oil and gas work processes.
Leveraging the Power of Spatial-Temporal Information with Graph Neural Networks as the Key to Unlocking More Accurate Flow Rate Predictions
Authors: Voskresenskii A. , Pakhomov M. , Katterbauer K. 
Published: October 2023

Abstract
The digital transformation of the oil and gas industry has prompted the need for improved forecasting methods to enhance efficiency, safety, and decision-making processes. This paper proposes a novel approach to oil flow rate forecasting by leveraging the power of Graph Neural Networks (GNNs) to capture complex spatial and temporal dependencies within oil and gas production systems. The study presents a graph representation of oil wells, employing GNNs to learn latent representations of the wells and their connections, while incorporating temporal convolution to capture time-dependent relationships. Experimental results show that the proposed GNN-based approach achieves higher accuracy in terms of mean absolute error and coefficient of determination compared to traditional time-series forecasting methods such as CRMs, Arps method, decision tree based models and other types of linear and autoregressive models. The findings highlight the potential of GNNs for accurately forecasting oil production, which can significantly impact production planning, resource allocation, and revenue estimation in the oil and gas industry.
Causes of Rapid and Extensive Fracture Propagation After Fracture Coalescence
Authors: Jin Yuteng , Misra Siddharth , Rougier Esteban 
Published: October 2023

Abstract
The paper identifies specific spatial arrangement of the existing crack pathways and the crack network at the time of crack coalescence that lead to a rapid and extensive crack growth immediately after crack coalescence. To that end, we engineer relevant and informative features from simulated crack network generated using the HOSS simulator and then train machine learning model to relate the most significant crack-network features with the onset of rapid and extensive crack propagation immediately after crack coalescence. These features are indicators of the underlying mechanism of crack propagation and coalescence. The features serve as the inputs of a SVM classifier with RBF kernel to find the decision boundary between the two types of samples, those that undergo rapid and extensive crack propagation after coalescence and those that don't. Through permutation feature importance evaluation, we determine the 7 most important crack-network features associated with sudden, large crack growth. Notably, these features are associated with the status of energy buildup, energy distribution, and energy release inside the material. The primary aim of this study is to explain the sudden and extensive fracture-propagation post coalescence in terms of the KDE-based and subgraph-based features. The underlying assumption is that the stress and energy state inside the material cannot be directly observed; hence, the phenomenon of coalescence and large fracture propagation post-coalescence need to be answered in terms of the mechanisms observed based on the spatiotemporal evolution of the crack network under the uniaxial compression.
Decarbonization of Offshore Oil and Gas Installations with Offshore Wind and Energy Storage Solutions
Authors: Laskar Karishma , Kumar Avinash 
Published: October 2023

Abstract
In light of India's commitments to COP26, there is a sense of urgency to accelerate the energy transition and to decarbonize the energy portfolio of oil & gas companies. This paper discusses the following decarbonization strategies; (a) Installing offshore wind farms and battery solutions for providing power to process platforms and commercialization of the remaining power, (b) Integrating solar with Li-ion energy storage solutions in unmanned platforms. This work outlines how a reliable energy storage solution can be the key to stepping towards this transition. The offshore unmanned platforms have conventional battery solutions such as: Lead-acid batteries - Installed to meet no sun days & also provide power during the night.Ni-Cd batteries - Integrated with primary power sources such as gas turbines provide power in manned platforms.
Conventional batteries have limitations in terms of energy density, frequent replacement period, footprint, and their heavy weight. With the introduction of renewable energy sources such as offshore wind & solar, a very reliable solution is the need of the hour to supply uninterrupted power during energy fluctuations due to wind speed or intermittency of solar and to limit the usage of carbon-emitting sources such as Diesel generators. This makes Li-ion batteries one of the favourable candidates for powering offshore installations where space is a constraint and due to its added advantages of high energy density and low maintenance requirements. It also discusses how offshore wind can be a potential candidate for a cluster of wells and process platforms where the power requirement is in Megawatts and extra power generated can be fed back to the onshore grid nearby through PPA agreements. A pilot project has been considered for the purpose of the study and different cost components to assist in techno commercial analysis of such offshore wind projects of 50 MW.
Integrated Carbonate Lithofacies Modeling Based on the Deep Learning and Seismic Inversion and its Application
Authors: Xin Chen , Deshuang Chang , Qing Liu , Qian Sun , Weixiang Zhong , Wenwen Yu , Xiaoliang Li , Hanzhou Fan , Qingning Yang , Dengyi Xiao , Fuli An , bo Wang , Lu Lv , Yu Peng , Qiang Liu , Kongzhi Huang 
Published: October 2023

Abstract
To improve the accuracy of carbonate lithofacies modeling, mainly well data such as core, thin section and well logging data had been adopted in conventional methods. Although the reservoir types classification is very detailed, but it is usually difficult to integrate with seismic data to make 3D lithofacies model. To address the issue of carbonate lithofacies modeling, a new integrated carbonate lithofacies modeling technique was summarized based on thin section, core, well logging, 3D seismic data and production performance data.
The integrated carbonate lithofacies modeling workflow mainly contains 5 steps. 1) Integrated lithofacies classification based on the core, thin section, well logging, FMI, CMR and production performance data. 2) Petrophysics lithofacies classification based on the cross-plot analysis between sensitive well log curves. 3) Petrophysics lithofacies prediction based on the sensitive well log curve by deep learning method, and verification by core lithofacies analysis. 4) Seismic inversion volume optimization by well lithofacies calibration. 5) Lithofacies modelling based seismic inversion based on the seismic inversion cut-off analysis (Fig.2). This workflow integrated seismic impedance (continuous variable) with lithofacies (discrete variable), and converts seismic inversion into lithofacies directly.
According to the certification of new wells, this technique had been applied successfully in carbonate reservoir of M oil field in Middle East, it not only improves the accuracy of 1D lithofacies prediction for wells by deep learning method, but also improves the accuracy of 3D lithofacies modeling for the whole oilfield by well and seismic inversion integrated. The lithofacies modeling not only matched with lithofacies from core analysis and petrophysics lithofacies prediction from well log analysis, but also matched with seismic inversion data in no well area.
The integrated carbonate lithofacies modeling workflow integrated thin section, core, well logging, 3D seismic data and production performance data, and improved the improves the accuracy of 3D lithofacies modeling for no well area. It’s useful for new wells optimization and high efficiency development with lower cost. The integrated carbonate lithofacies modeling workflow not only suit for carbonate reservoir, but also suit for clastic reservoir.