bluuebunny/crossref_metadata_2025_split huggingface dataset:
Field Applications of Quantitative Fracture Diagnostic From Distributed Temperature Warmback Analysis
Authors: Mao Yilin , Godefroy Caroline , Gysen Michel 
Published: January 2023

Abstract
Fracture diagnostic on a cluster scale of multi-stage hydraulic fracturing wells remains challenging but essential to determine the quality of the stimulation operation and the completion strategies for future wells. Since the stimulation fluid is injected at a different temperature compared to the original geothermal, the considerably modified and highly heterogeneous thermal profile after stimulation presents significant potential to serve for fracture diagnostic purposes. In this work, a model to analyze the temperature signal associated with the shut-in period after hydraulic fracturing is presented, along with the pilot testing of two datasets.
The model extends the scope of traditional thermal injection profiling algorithm with fracture diagnostic functions. During the development process, we incorporate the existing warmback model of conventional wells in analyzing shut-in temperature data with a newly developed stimulated region thermal model. Two main outputs of the model, the injection fluid intake and the fracture propagation extent, are estimated and tested. The model is then automated and thoroughly implemented in the software package.
The primary applications of this work are injection fluid intake and fracture propagation extent of each perforation cluster in fractured wells. The spatial resolution of the injection profiling and fracture growth can reach the sub-meter scale (same as the distributed temperature sensing spatial resolution). Compared to the conventional radial warmback model, the temperature signals from the fractured well show a much faster warming trend while taking relatively larger amounts of injection fluid. This behavior can be attributed to the additional heat loss to the unstimulated region and larger contact area between clusters. On the other hand, leak-off fluids create a cooler stimulated region around the fracture plane, which makes the warmback trend slower compared to the linear flow regime model. The model developed in this study considers both behaviors to simulate the actual datasets.
The inverse model estimates the fracture propagation extent in both the stimulated region as well as the fracture plane. Both estimations can jointly infer the leak-off extent of an individual cluster. As a pilot project, this model is tested on warmback temperature data from two datasets. The injection profiling results using the model are consistent with profiles obtained from other data sources, while the estimated fracture propagation extents of individual clusters present different types of fracture geometry (symmetrical, asymmetrical, double peaks, etc.).
Quantitative injection profiling and fracture propagation extent estimations of an individual cluster using warmback analysis have been proven viable and reliable in this field study. It could be the first quantitative warmback analysis applied to fracture wells in the industry.
CO2 Injection in a Depleted Gas Field
Authors: Lüftenegger Markus , Rath Alexander , Smith Michael , Danilko Alexey 
Published: January 2023

Abstract
A compositional dynamic simulation model is fully implicitly integrated with a gas injection surface network model, to study the effects of CO2 injection into a depleted gas field. Multiple prediction scenarios are evaluated, under uncertainty, to reduce risk and improve decision making. We propose a workflow, composed of a geological sensitivity clustering step followed by a dynamic calibration step. The aim of this workflow is to decrease the objective function and improve the reliability of a probabilistic forecast, to model the CO2 storage potential of an onshore depleted gas field. Each run, containing all parameters and its objective function was exported and introduced into an inhouse R Script. Within this script we train a random forest tree to predict the objective function for various parameter combinations. This random forest is then used to generate 1 million models with the initial distribution from the simulation runs and will predict their objective function. The idea here is to get to a posterior distribution that can be used in the second simulation iteration. This method achieves a better history match within the ensemble, in a vastly reduced timeframe. History matched models were taken forward to predict CO2 injectivity. Injection variables, facilities and well completions for several wells have been included in the analysis, and numerical reservoir simulation models have been integrated with a surface network.
A Data Driven P-V-T Model to Predict the Oil Formation Volume Factor, Solution GOR and Bubble Point Pressure for Characterizing an Oil Reservoir
Authors: Kumar Saket , Gautam Sandarbh , Thakur Nitu Kumari , Khan Murtaza Ahmed , Kumar Sikandar 
Published: January 2023

Abstract
Characterizing an oil reservoir requires one to understand the Pressure- Volume-Temperature (PVT) properties of reservoir fluids, especially bubble point pressure, solution gas oil ratio and oil formation volume factor because of its more often utilization in reservoir engineering studies. The current correlations are restricted by the use of sample from a particular field. As the physical properties and the composition of the crude oil varies the results becomes erroneous after a specific range. This correlation will give results only over a specific range of properties like specific gravity, viscosity, composition etc. The challenge is to develop a new approach which overcomes the current shortcomings. In this paper a new machine learning based model has been developed using Interactive Multivariate Linear Regression (I-MLR) method by integrating a large number of datasets to predict above mentioned properties. It overcomes the restriction of the previous correlations as it does not use data from any particular field. As such it is applicable over wide range of physical properties and composition. This model does not require any laboratory studies which makes it more economical. The validation of the model is done after detailed comparative study done with various commercially used empirical correlations.
An Effective Approach to Modeling Hydraulic Fracture in a Coarse Scale Simulation Model
Authors: Tello Gomez Adan Hanniel , Fekkane Abdelhafidh , Oluwa Jubril Babajide , Al-Qahtani Adel 
Published: January 2023

Abstract
The use of hydraulic fracturing to improve well productivity in tight reservoirs and connecting unconsolidated quality reservoirs via hydraulic fracturing is very well practiced in the oil industry. Currently to model hydraulic fracture in order to fully assess its effect in the well and its interaction with the formation (or different layers) requires upscaling methods to reach higher resolution (refinement) in order to reach certain accuracy in simulations like sector-based or local grid refinement techniques around well-reservoir modeling. However, modeling hydraulic fracturing in a large full-field reservoir simulation model with hundreds of wells and millions of cells is computationally very expensive. The aim of this proposal is to provide an alternative modeling of hydraulic fracturing at any size-based cell without the need of refinement saving computational cost.
Even though computational capacity has increase exponentially in the last decade, the complexity and resolution of simulation models does overwhelm conventional computational processing leaving high resolutions models to corporations and companies with major resources in computing as the only capable to ensure an effective simulation of stimulation in wells modeling interaction with full field reservoir models. The search of alternatives to simplify and propose representative models of hydraulic fracture is part of needed applications to provide tools in field development and reservoir management scenarios, specially if a sizable data base with good quality surveying and compensated with production and performance data can represent key cornerstones to create meaningful characteristic variables in order to apply in modeling and forecast activities as history matching.
This paper proposes a new approach to model the hydraulic fracturing (HF) effect based on a volumetric relation between created fracture and influenced volume in a designated reservoir/productive formation to be use specially in coarse simulation models to simulate the enhancement of the hydraulic fracture effects in production and history matching modeling. The use of field data and pressure matched properties based on stimulation parameters is the foundation to rely on. The new approach helped developing a fracture factor used to model permeability interaction between formation and stimulated volume designated by cell size and position. The proposed approach guaranties no change on regional nor alteration of static model properties maintaining the geological concept intact and providing a more realistic approach in order to simulate effectively stimulation in modeled wells in any type of grid or geometry of cells.
Evaluation of NMR Derived Capillary Pressure Curves Against Porous Plate Data for a Sandstone Reservoir
Authors: Sharma Sharanya , Duhe Craig , Gao Bo 
Published: January 2023

Abstract
NMR assisted capillary pressure and water saturation measurements have proven to be significantly faster than traditional porous plate and centrifuge methods. The NMR directly measures water saturation along the length of the core and associated capillary pressure generated at a given centrifuge speed. It overcomes limitations of requiring lengthy equilibration times, tedious material balance, and approximate solutions. However, limited data is available with respect to actual application and operating range of the technology in reservoir cores. The objective of this study is to evaluate data quality, identify best practices, and any limitations in utilizing the faster NMR derived capillary pressure curves when compared against the gold standard porous plate method.
Core samples from a sandstone reservoir were selected to test a permeability range between 400mD – 1600mD. Representative synthetic reservoir brine was used in the tests. All plugs selected for the NMR capillary pressure study had associated porous plate data available. NMR capillary pressure curve was generated using the GIT-CAP patented methodology for three, four, and eight centrifuge speeds to understand the impact of test speed on capillary pressure curve resolution. The tests were performed at net confining stress and at ambient conditions to evaluate if large differences were observed in results. Gravimetric measurements were simultaneously obtained to ensure data quality. Two different available imaging pulse sequences – SE-SPI and the SPRITE method were also evaluated for the different samples in generating the NMR assisted capillary pressure curves. This work contributes to outlining parameters for workflow optimization when adopting this methodology in real field applications.
Evaluating Complex Lacustrine Carbonate Reservoir by Integrating Advanced Logging Techniques and Core Measurements-A Case Study in Qiadam Basin
Authors: Chen Wen An , Yu Dai Guo , Liu Guo Liang , Li Qing Bo , Zhang Di , Zhao Meng , Wu Fang fang , Cai Liang , Zhao Xian Ran , Li Shen zhuan , Wu Jin Long 
Published: January 2023

Abstract
A complex and tight lacustrine carbonate reservoir is the main target reservoir in Qaidam Basin. The diagenesis of shallow-shore lake deposition has created a highly heterogeneous reservoir in both vertical and horizontal directions in terms of lithologies, pore structures, and permeability. The core analysis shows a wide variety of lithologies that include terrigenous clastics (quartz, orthoclase, and plagioclase), carbonate (calcite, dolomite, and ankerite), and evaporite minerals such as anhydrite, etc. Electron microscope analysis shows mainly intercrystallite pores with dolomite, dissolved pores, and microfractures. Associated with the complex lithology and pore structure, reservoir permeability is varied and is mostly less than 0.02mD. Evaluating and characterizing this complex reservoir to find a better pay zone was the objective of this study.
To evaluate the lithologies, pore structures, porosities, and permeability in quantities accurately, the advanced gamma-ray spectroscopy logs, nuclear magnetic resonance (NMR) logs, and high-resolution micro-resistivity borehole image logs were acquired in some key wells. Integrated with core data such as X-ray diffraction (XRD), mercury injection capillary (MICP), the lithofacies, texture facies, and pore facies were classified based on the minerals from gamma-ray spectroscopy, the textures from borehole image, and the pore sizes from NMR. The combination of these three facies represents the reservoir quality.
In the study area, the lithofacies were classified into four types, which are high carbonate content facies, middle carbonate content facies, high clay content facies, and high clastic content facies. The texture facies were classified into four types, which are massive texture, layered texture, dissolved texture, and algal texture. The pore structure facies were classified into four types, which are macro facies, meso facies, meso-micro facies, and microfacies. The better pay zone is the combination of high carbonate content facies, algal texture, and macro facies. The pay zone of the reservoir was well defined and mapped with the combination facies vertically and horizontally in the whole study area.
The integrated solution described in this study leverages the advantages of advanced gamma-ray spectroscopy, NMR, borehole images, and core measurements. It develops a comprehensive understanding of the complex carbonate reservoir and provides the solution to pinpoint the sweet spots and place the horizontal well.
Application of Machine Learning for Estimating Petrophysical Properties of Carbonate Rocks Using NMR Core Measurements
Authors: Farmanov Ravan , Feldmann Felix , Mathew Eric Sonny , Tembely Moussa , Al-Shalabi Emad W. , AlAmeri Waleed , Masalmeh Shehadeh , AlSumaiti Ali 
Published: January 2023

Abstract
Evaluation of petrophysical properties such as porosity, permeability, and irreducible water saturation is crucial for reservoir characterization to determine the hydrocarbon initially in place and further optimize hydrocarbon production. However, estimation of these parameters is challenging for carbonate rocks due to their heterogeneity. One of the ways to determine petrophysical properties is the use of nuclear magnetic resonance (NMR), which involves applying a magnetic field to the formation and detecting signals emitted from pore spaces. The main objective of this study is to develop an empirical correlation for porosity, permeability, and irreducible water saturation by comparing NMR and laboratory measurements for carbonate rocks in the Middle East. Furthermore, machine learning (ML) approach was applied to predict these petrophysical parameters utilizing NMR data. Different ML algorithms such as tree-based and neural networks were trained to estimate these petrophysical properties of carbonate rocks. The obtained results from ML algorithms were further compared with core measurements to ensure their accuracy.
The results showed that the use of T2 spectrum as an input provided more accurate results than NMR features. It can be proven by observing the performance of deep neural networks algorithm, where the models showed R2 values of 0.87 and 0.74 for porosity prediction using T2 and features extraction approaches, respectively. The same behavior was followed for the permeability estimations as deep neural networks model scored R2 = 0.81 (T2 approach) and R2 = 0.74 (features extraction approach). Similarly, determination of irreducible water saturation was more accurate using T2 approach (R2 = 0.87), whereas features extraction technique also exhibited a decent performance (R2 = 0.71). Also, T2 approach is more convenient since it is more straightforward to generate T2 spectrum from NMR measurements and use it for the ML models. Furthermore, based on the machine learning approach, gradient boosting and deep neural networks models performed with higher accuracy than other algorithms. This can be attributed to their strong configuration, which is able to find patterns between input and output parameters. This study provides more insight into petrophysical properties determined from NMR measurements in carbonates using ML techniques. This is useful in better characterizing carbonate reservoirs in the Middle East through accurate estimations of hydrocarbon resources and related reserves.
Abandoned Exploration and Appraisal Wells Turned into Long-Term Monitoring Wells for Enhanced Far-Field Reservoir Characterization: Lessons Learned from a Frontier Deepwater Installation
Authors: Mimoun Jordan G , Gagnon Jeffrey Paul , Zeilinger Sabine C , Fabuel-Perez Ivan , Horsfall Donald Isaac 
Published: January 2023

Abstract
This paper discusses benefits, caveats, and lessons learned of wireless pressure monitoring systems in abandoned exploration and appraisal wells to gain insight into reservoir properties that extend beyond the scope of standard well testing.
Appraising large offshore deepwater discoveries requires drilling and evaluating several wells across a field to increase subsurface understanding and reduce development uncertainties. The recent technological advancement of wireless pressure monitoring systems, which utilize electromagnetic telemetry, has enabled operators to capitalize on previously untapped value from exploration and appraisal wells by turning them into long-term observation wells, without compromising abandonment compliance. Even if not tested, these penetrations can provide valuable dynamic data for enhanced reservoir characterization early in the appraisal life cycle and at little incremental cost.
Such a system was installed in a deepwater well in Cyprus to further understand potential compartmentalization risks. A nearby well test, executed 3 years after system installation, was successfully monitored by the abandoned penetration. The interference test helped shed light on far-field connectivity, inter-well reservoir properties, and connected volume. This information will improve the fidelity of the geological and reservoir simulation models utilized to inform depletion planning and support field development and commercial decisions.
This paper presents novel findings to help ensure data quality and representativeness from such systems, focusing on the overprint of the wellbore environment on the reservoir signal. Specifically, the deployment in a cased wellbore left with drilling fluid revealed lengthier and more complex swap-out dynamics with reservoir fluid than anticipated. As with most new technologies, subsequent installations benefited from these valuable lessons learned, resulting in more robust and reliable implementation practices.
Numerical Simulation Modelling of Well Tests for a Frac-Pack Completion Well in a Shallow Sand Reservoir
Authors: Mousavi Mirkalaei Seyed Mousa , Motaei Eghbal , Husen Akbar Ali Anwar 
Published: January 2023

Abstract
Understanding the flow behaviour of fractured wells is crucial to operators and service companies in evaluating the effectiveness of stimulation work performed on the well. New insights in modelling of well transient pressure tests in hydraulic fractured unconsolidated sand is presented in this paper by utilizing 3-D numerical black oil simulation in single and two layered sand reservoirs with a thin shale layer in between. The upper layer perforated and fractured to treat the sand production as frack-pack technique and the well test has been conducted only on the upper layer. Porosity and permeability heterogeneities are classically evaluated from petrophysic well log measurements and through geological description of the reservoir, then possibly refined by simulation and history matching. The pressure measured in the well test in four cycles of drawdown and build up. The well bottom hole pressure (BHP) behaviour cannot be adequately described with conventional well tests analysis for the upper sand without including the flow from the lower sand.
Different scenario of production from upper with adding hydraulic fractured examined to match the oil/gas production and bottom hole pressure. A range of factors are examined that may impact the introduced fracture flow behaviour based on actual fractured well flow. The main fracture and reservoir parameters investigated include absolute permeability of upper layer, gas oil contact (GOC), relative permeability endpoints to oil and gas, hydraulic fracture properties (permeability, width) and extension and finally the skin factor.
The results of dynamic simulation model show that the model is very sensitive to the amount of gas production and hydraulic fracture vertical extension. We highlight through this example and sensitivity simulations that the GOC should be very close to the well preformation or else the pressure could not be matched. Hydraulic fracture vertical extension is required for matching of BHP and gas rate, without it, the gas rate will be very high in all of the simulation cases. The fracture connecting the upper layer to lower layer with only upper layer perforated. Absolute permeability from log cannot represent to the real permeably measured from well test. To match all historical data absolute permeability, need to be reduce by one order of magnitude. Finally, the model is sensitive to the skin factor for matching of pressure build up.
The main business questions were answered through integrated analysis of the analytical well model and dynamic simulation of single model to identify the source of excess gas and understand the well performance to reduce the uncertainty in production forecast. Fast approach in the single well modeling and efficient approach in the integration in the workflow is described in detail in the paper.