bluuebunny/crossref_metadata_2025_split huggingface dataset:
Curing Total Lost Circulation on Deepwater Well by Pumping Multi-Purpose Cement Plug through Side Sub
Authors: Spagnolo S. , Abdallah A. , Emam M. , Abdulaa Ahmed , Younan Joseph , Bogaerts Martijn , Fayed Mohamed 
Published: February 2024

Abstract
Loss of circulation is one of the most challenging complications during drilling a well. The problem is even more severe in exploratory wells due to the absence of a clear understanding of the formation pressure and the presence of any high-pressure zones that may cause well control problems due to loss in hydrostatic pressure associated with loss of circulation. Furthermore, the problem will have a substantial budgetary impact, especially in deep water environments, considering the high running cost of the rigs along with all the side services that must be on standby on the rig. With the operator's decision to drill a sidetrack from the original well, an engineered cement slurry was designed to cure losses, plug and abandon the original hole, and act as a kick-off point for another trajectory. The cement job was intended to be pumped and displaced through the drilling assembly and through the side circulating sub. After the planned wait on the cement period, the plug was successfully tagged, pressure tested, and used as a kickoff point to start drilling the sidetrack section. The purpose of this paper is not to recommend such a procedure but to discuss a successful case study and the mitigations and measurements that must be taken and ensured before deciding to execute such jobs.
Successful Utilization of Managed Pressure Cementing During Running and Cementing 7 Inch Liner Through Rotating Control Device in a Narrow Mud Weight Window for an Unconventional Well
Authors: Alzahrani Z. G. , Albaraa A. A. , Iturrios C. O. , Aldhaif M. 
Published: February 2024

Abstract
Formations with a narrow mud window between the pore pressure and fracture gradient have always imposed a significant risk on well integrity during drilling and post-drilling operations. One of the main risks associated with such formation behavior is complete or partial loss of returns while drilling or cementing leading to many counterproductive consequences. For example, complete or partial loss of drilling fluid while drilling a hole section could reduce the hydrostatic pressure to below the bottom hole pressure resulting in the intrusion of formation fluid into the well bore. Another major risk is differential sticking of drilling and casing strings while performing well control securement procedures or by being stationary for any other reason. As a result, Managed Pressure Drilling (MPD) system was introduced to drill an 8-3/8" hole section in an unconventional well with a 3,500 ft interval that has a narrow mud window. The well was drilled with 106 pcf while maintaining an EMW of 120 pcf where the required overbalance is met. Other wells on the same area have suffered tremendously while drilling the same interval conventionally yielding high non-productive time which proved the concept of utilizing MPD system as a pro-active solution instead of reactive once needed. The next major challenge was to run and cement 7" liner to achieve the required cement barrier for isolating the problematic interval. Losses or well control while cementing 7" liner would compromise the integrity of cement barrier. Therefore, to mitigate losses and keep the required overbalance simultaneously during running and cementing 7" liner, Managed Pressure Cementing (MPC) was the effective solution.
This paper illustrates the entire procedure and results of successfully utilizing MPD system for running and cementing 7" liner string twice in a challenging environment where various stuck pipe and well control incidents occurred. Despite the fact of the extra budget and time for utilizing such systems, the increased safety risks, such as influx or well flow, and the extra cost that are associated for fixing what a stuck pipe can create is much higher.
Dispersion Corrections on LWD Quadrupole Data: Methods and Results
Authors: Schimschal S. , Geerits T. , Wu L. , Sy R. 
Published: February 2024

AbstractIn slow formations borehole acoustic Logging While Drilling (LWD) operations, it is common to obtain formation shear slowness from the dispersive borehole quadrupole wave. Due to poor signal to noise ratio and/or tool eccentricity effects, it is not always possible to obtain formation shear slowness directly via conventional Slowness-Time-Coherency (STC) methods. Consequently, a dispersion correction is frequently needed to QC and/or correct the STC result. We propose a hybrid method that allows for a model-based (MB) as well as a phenomenon-based (PB) approach. The latter is ideally suited to address, identify, and overcome the limitations and dependence of the former, on tool eccentricity, tool model, inaccurate knowledge of borehole fluid slowness, formation anisotropy (Vertical Transverse Isotropy), and other unknown acquisition circumstances.
Influence of Acid Type on Wormhole Generation and Carbonate Rock Properties
Authors: Alomair A. , Almubarak T. , Alqahtani N. , Caliskan Sinan 
Published: February 2024

Abstract
Matrix acidizing is one of the most effective techniques to restore formation permeability and increase production rate. It is typically performed using different types of treatments, such as mineral acids, organic acids or chelating agents. These stimulation treatments tend to generate pathways known as wormholes through a chemical reaction with target carbonate formations. Bottom hole conditions play an important role in the treatment selection since they influence the reaction rate, wormholes structure and wellbore integrity. This work shows an experimental study of the impact of acid type on wormholes generation in carbonate rocks at different temperatures.
Three different acids were used which were hydrochloric (HCl), formic (HCOOH) and acetic (CH3COOH) acids. These acids were injected at a fixed rate of 2 cm3/min into Indiana Limestone core plugs at two different temperatures: 150 and 250°F using coreflood equipment. Non-destructive visualization and analyses were conducted by using the Computerized Tomography (CT) scanning technique, which provided wormhole patterns, slice images with a thickness of 625 micron as well as statistical comparisons. Furthermore, dynamic acoustic velocities and elastic parameters were determined at different confining pressures for the post treatment core plugs to compare their values.
The use of different acids at the same temperature conditions formed different pathways and patterns of wormholes. In addition, the temperature effect has a clear relationship with the required pore volume to breakthrough (PVTB). At low temperature, HCl demonstrated an outstanding performance by having the least required PVTB, while at high temperature acetic acid showed an improved generated wormhole pathway compared to the other acid types. Nonetheless, the different acids did not show a significant impact on the dynamic mechanical properties of the rock samples.
This work evaluated the effect of temperature on the rock wormholes patterns using different types of acids based on their dissolving power. The study concluded that the temperature has a significant effect on carbonate matrix acidizing operations. The study also focused on how the selection of the acid type can make a huge impact related to bottom hole conditions.
Multi-Level Transfer Learning for Optimal Features Prediction
Authors: Sajid Muhammad 
Published: February 2024

Abstract
The quality of the final predictions in machine learning (ML) depends on a multitude of factors, and among these factors, the generalization of features within the input dataset plays a pivotal role. This generalization is particularly crucial when dealing with seismic data, as the complexity of the underlying geological structures and the seismic characteristics themselves exhibit substantial variability across different geological study fields. Such variations pose unique challenges when it comes to model training and achieving optimal predictions for specific seismic features. Considering these challenges, the research proposes a comprehensive approach that involves the application of three stages of transfer learning. This approach has been designed to facilitate the seamless convergence of the model towards an optimal level of adaptability for the extraction of seismic features. Each step of transfer learning in this method incrementally enhances the model's ability to accommodate the idiosyncrasies of seismic data from diverse study fields, ultimately leading to the desired seismic feature prediction. The research seeks to improve the overall accuracy and robustness of seismic feature prediction across varied geological contexts, thereby contributing to the advancement of seismic data analysis and geological interpretation.
Physics-Informed Neural Networks for Modeling Flow in Heterogeneous Porous Media: A Decoupled Pressure-Velocity Approach
Authors: Alhubail Ali , Fahs Marwan , Lehmann Francois , Hoteit Hussein 
Published: February 2024

Abstract
Physics-informed neural networks (PINNs) have shown success in solving physical problems in various fields. However, PINNs face major limitations when addressing fluid flow in heterogeneous porous media, related to discontinuities in rock properties. This is because automatic differentiation is inadequate for evaluating the spatial derivatives of hydraulic conductivity where it is discontinuous. This study aims to devise PINN implementations that overcome this limitation. This work proposes decoupling the mass conservation equation from Darcy's law and utilizing the residuals of these decoupled equations to train the loss function of the PINN, rather than using a single residual from the combined equation. As a result, we circumvent the need to find the spatial derivative of the discontinuous hydraulic conductivity, and instead, we impose the continuity of fluxes. This decoupling necessitates that each primary unknown (pressure and velocity components) be computed by the neural networks (NNs) rather than deriving the velocity (or fluxes) from the pressure. We examined three NN configurations and compared their performance by analyzing their accuracy and training time for various 2D scenarios. These scenarios explored various boundary conditions, different hydraulic conductivity fields, as well as different orientations of the heterogeneous media within the domain of interest. In these problems, the pressure and velocity field are the primary unknowns. The three configurations include: (a) one NN with the three unknowns as its outputs, (b) two NNs, one outputting pressure and the other outputting the velocity, and (c) three NNs, each having one primary unknown as an output. Utilizing these NN architectures, we were able to solve the heterogeneous problems with varying levels of accuracy when compared to results from numerical simulators. While maintaining a similar number of training parameters for a fair assessment, the configuration with three NNs yielded the most accurate results, with a comparable training time to the other configurations. Using this optimal configuration, we performed a sensitivity analysis to demonstrate the effect of modifying the NN(s) hyperparameters, such as the number of layers, the number of nodes per layer, and the learning rate, on the accuracy of the results. We introduce a novel PINN approach for modeling fluid flow in heterogeneous media. This proposed method not only preserves the inherent discontinuity of rock petrophysical properties but also leverages the benefits of automatic differentiation. By incorporating this PINN architecture, we have opened up new possibilities for extending the application of PINN to realistic reservoir simulations that capture the complexities of the subsurface.
Breaking New Ground: Exploring the Connection Between Drilling Parameters and Formation Properties through Advanced Multi-Source Data Pattern Analysis
Authors: Abouhamed Moustafa , Elmasry Hesham , Borayek Ahmed 
Published: February 2024

Abstract
In the oil and gas industry, an extensive volume of logging data is routinely generated and acquired during operations. This data is accumulated across various phases of exploration, production, and subsequent operations. The challenge lies in extracting meaningful insights from this high-dimensional dataset. Constraints in data visualization (such as the three spatial axes, x, y, and z) and manipulation further complicate the process of drawing valuable conclusions.
As a method to tackle this challenge, a technique has been devised to streamline data visualization by reducing high-dimensional data to a lower dimensionality, while retaining vital features crucial for big data analysis. Principal component analysis (PCA) emerges as a prominent approach employed for data dimensionality reduction. PCA simplifies multivariate datasets encompassing well-logging tools (e.g., gamma ray, density, neutron porosity, and resistivity) and drilling parameters such as rate of penetration, weight on bit, and vibration data, condensing them into a reduced number of factors known as principal components (PCs).
The results of the dimensionality reduction technique show that various logging data and drilling parameters were condensed into a set of principal components (PC1, PC2…PCx), where x corresponds to the number of utilized variables. The analysis indicates that the first two components (PC1, PC2) capture most of data patterns. The graphical representation of these components (PCA biplot) reveals distinct clusters with clear patterns, facilitating the identification of separate electro-facies. Moreover, PC1 exhibits a strong correlation with lithology variations, enabling its utilization in well-to-well correlation. Additionally, regression analysis demonstrates a significant predictive relationship between PCA components and well logging variables, allowing the use of the R-squared regression technique to forecast a result curve based on PCA input. Higher principal components are found to be more associated with formation fluid, thus complementing the standard petrophysical analysis.
The industry's current shift is from data collection to practical applications. By employing diverse dimensionality reduction techniques, the workflow enables the analysis of big data in a more comprehensive manner, unveiling concealed trends and insights. This approach not only facilitates machine-learning and artificial-intelligence applications but also enables a deeper understanding of the data through descriptive forms, thereby supporting industry advancements.
Predicting Subsurface Reservoir Flow Dynamics at Scale with Hybrid Neural Network Simulator
Authors: Maucec Marko , Jalali Ridwan , Hamam Hassan 
Published: February 2024

Abstract
In this paper we demonstrate the application of state-of-the-art deep learning using hybrid neural networks (HNN) that generalize and scale to multi-million, structurally diverse reservoir model grids and generate long-term spatio-temporal predictions of fluid and pressure propagation. The HNN simulator (HNNS) is a surrogate framework that consists of a subsurface graph neural network (SGNN) to model the evolution of fluids, and a 3D-U-Net to model the evolution of pressure. We benchmark the HNNS with two conceptually different reservoir models: a) modified SPE-10 model, with approx. 1 million grid size and variable number and positioning of vertical producers and injectors, b) synthetic fractured model, 15+ million grid size and 100+ injector and producer wells with variable geometry. We construct the network graph, where graph objects (nodes), representing reservoir grid cells are encoded with tens of static, dynamic, computed (relative permeability, gradients) and control (well rates) features. The graph edges represent interactions between the nodes with encoded features like transmissibility, direction and fluxes. We implement sector-based training with multi-step rollout to avail for the use of large-scale models. To properly perform the sector-based training the masking of sector boundary effects, sector stride and mixing sectors were used. We present the comparative results between the HNNS and the full-physics simulation for up to 30-year prediction of the 3D flow dynamics.
Production Analysis of Offshore Gas Wells (UK North Sea) Using Bootstrapped Arps Method: Reserves Estimation Without Reservoir Model
Authors: Afagwu C. C. , Weijermars R. 
Published: February 2024

Abstract
This study analyzes recently released proprietary daily production data from 31 decommissioned dry gas fields located on the UK Continental Shelf (UKCS). First, the hydrocarbon fields in the UKCS region were assigned to five groups, based on their geological source origin. The data analyzed here are from two lease areas comprising gas fields, sourcing from two of the five groups distinguished. One set of wells produced from Carboniferous Westphalian sandstones, the other from Permian Rotliegendes sandstones: both reservoir types remain active sub-salt targets. The production profiles of the 31+ legacy wells analyzed were grouped into four types: (1) Wells part of multi-well clusters producing from large fields, with production output controlled by facility access allocation; (2) Problem wells, with poor production and long downtime; (3) Wells with frequent shut-in days but excellent recovery; and (4) Fast clean producers; single well production profiles showing continuous decline without major interruptions, other than slight adjustments of choke settings. Two Permian gas fields (Mimas and Tethys) and one Carboniferous gas field (Kelvin) were produced with fast and clean producer wells. These wells appeared also equipped with bottomhole gauges which together with choke-settings and wellhead pressures, gives a comprehensive record of historic well performance. The historic well performance was analyzed using various bootstrapped data sets (6, 12- and 18-months production), which were least-mean-square-error (LMSE)-fitted with a traditional Arps decline curve analysis (DCA) method. The EUR forecast based on the bootstrapped Arps DCA (6, 12, 18 months) was compared with the known actual EUR based on the full historic well performance record. Based on our evaluation, a fast LSME fit of Arps DCA on early bootstrapped well data for 18 months appeared to give reliable estimates for the actual long-term EUR/well for two fields (Kelvin and Tethys). However, a third field (Mimas) required 5 years of bootstrapped data to achieve reliable EUR estimates.
Experimental and Numerical Study on the Phase Behavior and Distribution Characteristics of Oil-CO2-Water Three-Phase Fluid During Pre-CO2 Energized Fracturing in Shale Reservoirs
Authors: Han Xiao , Song Zhaojie , Deng Sen , Li Binhui , Li Peiyu , Zhang Lichao , Song Yilei 
Published: February 2024

Abstract
Analogous to the technique CO2 huff-n-puff, it has been determined that the preliminary injection of a predetermined volume of supercritical CO2, serving as a pre-fracturing fluid, holds significant promise in augmenting EOR and facilitate carbon storage from shale oil reservoirs when applied prior to hydraulic fracturing procedures. However, regardless of whether it's pre-CO2 energized fracturing or post-hydraulic fracturing CO2 huff-n-puff, the coexistence of oil-CO2-water in shale reservoirs leads to complex phase behavior and flow characteristics. To this end, this study firstly designs and conducts three-phasic experimental tests involving shale oil, CO2 and water. This is achieved by varying the order of CO2 and water injections into the shale oil to simulate both pre-CO2 energized fracturing and post-hydraulic fracturing CO2 huff-n-puff. Subsequently, based on the results of PVT experimental, further established a phase behavior calculation model of oil-CO2-water and used it to construct a numerical simulation model that takes into account the stress sensitivity of the SRV transformation zone, formulated the injection and production parameters of pre-CO2 energized fracturing and explored the fluid distribution mechanism at different stages. The equilibrium experiments and numerical simulations indicate that due to the dissolution of CO2 in water, the presence of the aqueous phase reduces the solubility of CO2 in oil, thereby diminishing the interaction intensity between CO2 and crude oil. The saturation pressure under the coexistence of oil-CO2-water three-phase behavior is lower than that of the oil-CO2 system. Furthermore, this phenomenon becomes more pronounced with the increase in water saturation. For pre-CO2 energized fracturing, compared with post-hydraulic fracturing CO2 huff-n-puff, the degree of dissolution of CO2 in water can be significantly reduced, which is conducive to the dissolution of CO2 in oil. Therefore, pre-injection of CO2 can maximize the use of CO2 and reduce unnecessary waste. Pre-CO2 energized fracturing can make CO2 spread more widely, effectively promote contact with oil to improve oil physical properties, and inhibit CO2 flowback ratio during the drainage process, which is conducive to carbon storage. Compared to the conventional CO2 huff-n-puff technique, pre-CO2 energized fracturing in horizontal wells can markedly ameliorate crude oil recovery. This research enhances our understanding of the oil-CO2-water phase behavior and fluid distribution with pre-CO2 energized fracturing in shale reservoirs, potentially offering insight for efficient shale oil reservoir development.
Polyols: A Green, Non-Toxic, High Temperature Stabilizer for Hydraulic Fracturing Fluids
Authors: Almubarak T. , Alabdrabalnabi M. I. , Albaiz A. , Yami M. Al 
Published: February 2024

Abstract
High-temperature fracturing fluids are in demand as we explore deeper and lower permeability reservoirs. Fracturing fluid stabilizers are divided into three categories: crosslinking delay additives, pH buffers, and reducing agents. Current technologies pose risks due to toxicity concerns and their harmful environmental effects. Consequently, the industry is continuously pushing towards green solutions. This work showcases the use of low-calorie sweeteners such as polyols as an environmentally friendly fracturing fluid additive.
The paper presents a guar derivative fracturing fluid system prepared at pH 10. The tests were performed in a high-pressure/high-temperature (HPHT) rheometer to evaluate the rheological performance at 300°F for 1.5 hours. The rheological schedule measured viscosity at 100 s-1 shear rate with varying lower shear rates ramps in between. Experiments are completed to recognize how polyols influence the rheological behavior of the fracturing fluid under a variety of crosslinker concentrations.
The results showed that using polyols can function as a crosslinking delay additive to control viscosity buildup, thus aiding in high-temperature stability. This was determined by varying the ratio of polyol to crosslinker concentration. Achieving a balance between the polyol and crosslinker concentration produced fracturing fluids that maintain a viscosity above 300 cp for 1.5 hours at temperatures of 300°F. Common oxidizer breakers were evaluated with this system and exhibited a controlled viscosity breakdown profile at these conditions. Proppant settling tests were also shared and showed the ability to suspend proppant for the duration of the treatment.
This study shares the use of polyols in fracturing fluids. Polyols not only provide excellent crosslinking delay properties but also protect organisms and the environment by mitigating the toxicity and health-damaging effects of commonly used high-temperature stabilizers.