bluuebunny/crossref_metadata_2025_split huggingface dataset:
Determination of Water Saturation and Porosity in a Carbonate Formation Using Mud Logging Data
Authors: Alkhafaji M. 
Published: February 2024

Abstract
The economic and operational limitations in formation evaluation based on well logging operations have revealed the exigency of a technique that provides detailed information on the target formations. At the same time, it is more economical and less troublesome in different well designs and operational environments. However, there was always room for uncertainty in the quantitative application of mud logging in formation evaluation. Thus, the mud logging industry has introduced more developed and improved systems of mud gas acquisition and data analysis that can provide high-quality information about the subsurface formations. This paper discussed a new approach to determine real-time porosity and water saturation from mud logging data in a cretaceous carbonate formation. Drilling, mud logging, and wireline logging data, from four wells penetrating the Mishrif reservoir, were used. The results showed a good match between the interpretation resulted from mud logging data and that from wireline logs data. Feasible applications of this technique can be very useful in cost reduction and optimizing the design of logging programs for unstable hazardous formations and horizontal development wells where it is risky and costly to run open hole logs. Furthermore, it can be used as a potential tool for evaluating low-resistivity reservoirs.
Time-Dependent Surface Mechanical Properties of Shale During Acid Fracturing: Microindentation Insights
Authors: Rached Rached M. , Haider Syed , Ghreizy Abdullah , Maalouf Elsa 
Published: February 2024

Abstract
Acid fracturing is used to enhance oil and gas production in tight carbonate formations. Fractures are created, and flow paths are etched on the surfaces, allowing for sustained flow under fracture closure stress. The induced fractures aperture and stability depend on the time-dependent deformation of the contacting asperities. In this study, we investigate the effect of acid exposure on the time-dependent deformation of the rock surface using micro-indentation testing.
Microindentation testing is used to investigate the change in the mechanical properties of cores extracted from the Eagle Ford surfaces following acid exposure for 1 hour and 24 hours. Specifically, time-dependent effects are captured by applying a constant load to a small area of the rock surface for a set period of time. Contact creep compliance rates are calculated from the measured deformations and contact areas. Our tests are conducted on both unaltered samples and samples exposed to varying acid strengths for different time intervals. We conduct multiple measurements on each core surface to minimize the effects of grain-scale heterogeneity.
The results demonstrate that the contact creep modulus varies in a fluid- and time-dependent manner. Specifically, a one-hour soak in deionized (DI) water or weak acid (pH=4) led to an increase in the contact creep modulus, signifying reduced creep. Conversely, a sharp decrease was noted after a one-hour soak in a strong acid solution (pH=2). Additionally, it was observed that creep deformation varies with the logarithm of time. Unlike previous studies, the data indicate that the relationship between acid strength and soaking time on creep behavior is complex and merits additional scrutiny, particularly for extended soaking periods in strong acids. These observed trends have significant implications for the long-term behavior of fractures, influencing factors such as proppant embedment and the rate of fracture closure under varying fluid exposure conditions.
This study reveals the critical role of coupled hydro-chemo-mechanical processes in controlling fracture closure during acid fracturing. The significant effect of creep on closure is highlighted, with novel experimental workflows employed using microindentation. Our study provides new insights into the behavior of shale under acid exposure and offers a framework for further research in optimizing acid fracturing.
Impact of Heterogeneity on CO2 Storage in Carbonate Saline Aquifers
Authors: Nolting Andrea F. , Eleftheriou George A. , Alqassab Hisham M. , Terrell Martin J. 
Published: February 2024

Abstract
This paper provides an investigation into the feasibility of carbon storage in saline aquifers, with a specific focus on the potential of injecting CO2 into heterogeneous carbonate rock aquifers that feature both conventional pore type system (referred to as the matrix) and secondary dissolution pore systems (non-matrix). Key areas of interest include the storage potential of the saline aquifer, injectivity of the wells, and the shape and size of CO2 plumes at the end of injection as well as several decades after injection.
To explore the potential of carbonate aquifers for CO2 storage and investigate the complex nature of carbonate rocks on CO2 migration and storage efficiency, the investigation builds twelve distinct geological scenarios that entail varying the quality of matrix systems into three distinct matrix sets: low-quality, moderate-quality, and high-quality matrix porosity and permeability properties. In addition, different non-matrix abundance is superimposed on the three matrix sets, producing significantly different levels of permeability contrast between matrix and non-matrix pore systems. Sensitivity of the results to fluid properties and rock-fluid interactions, such as different scenarios of displacement functions, salinity, and vertical transmissibility, are also tested and benchmarked.
The study demonstrates that non-matrix properties and distribution plays a crucial role in both injectivity and CO2 plume size, particularly in low-quality matrix conditions. The research also highlights that geological heterogeneity significantly affects plume growth and vertical distribution, particularly when there is a substantial contrast between matrix and non-matrix reservoir properties. Additionally, the most effective storage efficiency and minimal plume overprint occur when the matrix is of moderate quality (8%-24% porosity) and non-matrix features are present in low abundance.
CO2 Mass Transfer in Porous Media: Implications for Long-Term Carbon Sequestration in Saline Aquifers
Authors: Basilio E. , Addassi M. , Truscott T. , Hoteit H. 
Published: February 2024

Abstract
Carbon sequestration in saline aquifers is considered a suitable alternative for reducing anthropogenic carbon dioxide (CO2) emissions into the atmosphere. As supercritical CO2 is injected into the subsurface, its density difference with the residing water causes a CO2 buoyant plume to overlay a water-saturated porous layer. The study of diffusive and convective mechanisms during CO2 sequestration in aquifers is paramount for understanding the effects of solubility trapping to determine the long-term fate of the injected CO2. We studied the CO2 dissolution in aqueous solutions in porous media by coupling two experimental techniques: (1) constant pressure method and (2) visualization tests. Each test was performed in a closed visualization cell for at least 36 hours. Capillary tubes filled with glass beads of specific grain sizes are saturated with pH-sensitive solutions. CO2 is continuously supplied into the visualization cell to keep the pressure of the system constant by compensating the gas mass transfer into the aqueous phase. The injected gas is monitored in real- time. Furthermore, as the CO2 mass transfer into the aqueous phase takes place, an acidic solution is created, altering the color of the pH-sensitive solution. The change in color is continuously monitored. Our experimental approach allowed us to study the mass transfer dynamics in two different conditions: (1) diffusive mass transfer with the aid of natural convection; (2) diffusive mass transfer with the isolation of density-driven convection. The diffusive mass transfer was described by measuring the velocity of the interface separating the gas and aqueous phases. Natural convection was characterized by the onset time of convection and flow dynamics. We studied the effects of salinity (NaCl) and the impact of grain size in porous media systems. Results show that as average grain size diameter decreases, natural convection has no enhancing effects on the dissolution process when compared to a diffusive mass transfer process alone. We observed that convection onset time increases as grain size decreases, which in turn, diminishes the mass flux of convection. Salinity has been observed to play an essential role in the convection onset time as it affects parameters such as density, effective molecular diffusivity (affected by pore size), and viscosity. This work introduces a novel experimental approach based on coupling pressure-driven and light transmission-driven techniques to evaluate the mass transfer of CO2 in aqueous solutions in porous media. Results obtained in this study are essential to assess solubility entrapment and its long-term storage integrity.
Novel Physics-Informed Machine Learning Approach to Estimate Safe Mud Window Using Petrophysical Logging Data
Authors: Gowida A. , Elkatatny S. 
Published: February 2024

Abstract
The allowable limits of mud weights for drilling O&G wells, known as the safe mud window (SMW), play a crucial role in preventing wellbore instability issues and loss of circulation. The SMW consists of the minimum mud weight for shear failure (MWBO) and the maximum mud weight for tensile failure (MWBD), which are determined by the principal stresses of the formation, including the maximum (Shmax) and minimum (Shmin) horizontal stresses. Measuring these stresses accurately can be achieved through field tests or approximated using physics-based equations. However, obtaining the necessary in-situ geomechanical parameters for these equations, such as static Poisson's ratio and static elastic modulus, is not always feasible for all wells. Furthermore, the existing machine learning models rely on expensive and destructive tests.
To address these challenges, this study investigated the feasibility of utilizing machine learning (ML) algorithms to predict these parameters in a time- and cost-effective manner. New ML-based models employing artificial neural networks (ANN) were developed to predict the SMW limits (MWBO and MWBD) using petrophysical well-log data as inputs. A comprehensive dataset consisting of field test data and petrophysical logging data was collected and extensively analyzed to train the models.
The predictions generated by the developed ANN-based models exhibited a high degree of accuracy, with a mean absolute average error (MAPE) of less than 0.30% when compared to the actual output values. Thes developed models were validated using an unseen dataset, demonstrating remarkable agreement with the actual stress gradient and SMW limit values. The prediction accuracy exceeded 95%, and the MAPE was as low as 0.59%. The statistical analysis of the results confirmed the robustness of the developed equations in accurately predicting the SMW limits, provided that the logging data are available.
The originality of this research lies in its ability to efficiently and affordably predict the safe mud window (SMW), thereby mitigating drilling problems such as borehole instability. The developed models provide a reliable tool for accurately determining the SMW, surpassing the conventional methods that are more time-consuming and costly.
Interpretable Machine Learning for Prediction of Minimum Miscibility Pressure in CO2-Oil System Considering Nano-Confinement Effect
Authors: Wei Bing , He Yujiao , You Junyu , Wen Shuqin , Tang Jinyu 
Published: February 2024

Abstract
The determination of the minimum miscibility pressure (MMP) in CO2-oil systems is critical for modeling CO2-EOR processes experimentally and numerically. Nevertheless, in nano-confined space, the existing experimental and empirical formula methods present limitations regarding the utilization conditions and prediction accuracy respectively. Thus, in this study, a novel approach combining ML model with Shapley Additive Explanations (SHAP) algorithm is introduced, which aims to provide more precise and physically correct estimates of the MMPs considering the influence of nano-confinement.
A database containing MMPs in CO2 injection process under different conditions is firstly established based on 348 samples collected from experimental results and open publications. The input parameters determining MMPs include reservoir temperature, pore size, and oil composition. In this framework, XGBoost and MLP are used to mimic the input-output relations of the database. Then, SHAP is employed to comprehensively interpret the impact of the inputting factors on the MMPs by calculating the SHAP values.
The present study revealed that both the proposed XGBoost and MLP models exhibited R2 score exceeding 80% and demonstrated good predictive accuracy, as evidenced by small MAE, MSE, and MAPE values. Moreover, a comparative analysis of the SHAP interpretation results of the two models revealed that the explanatory patterns of the MLP model were more consistent with established physical laws, thereby rendering it more suitable for constructing an MMP prediction model based on the dataset employed in this investigation. It is noteworthy that although the SHAP interpretation of the XGBoost model did not entirely conform to actual physical laws, the influence of pore size on MMP followed the same pattern as elucidated by the MLP model. Specifically, within the nano-confined spaces, MMP decreased as the pore size decreased, and the pore size played a crucial role in predicting MMP (ranking first in the XGBoost model and second in the MLP model).
The outcomes demonstrate that the developed interpretable machine learning framework, which incorporates the effects of nano-confinement, can accurately predicts MMP under diverse conditions while maintaining the consistency of physical laws. Consequently, this framework offers valuable insights for the implementation and optimization of CO2-enhanced oil recovery processes.
Advanced Regularization Strategies and Applications in Double Complex Surveys of Qaidam Basin
Authors: Tan Chenqing , Ou Rongsheng , Yan Yufei , Li Xiang , Zhao Changlei , Heng Hailiang , Li Rong , Liu Lianlian , Cui Jie , He Jiao , Wu Chuan , Tong Yanming 
Published: February 2024

Abstract
The processing of land surface-seismic survey data in the Qaidam Basin has for many decades been recognized as facing the dual challenges of both surface and subsurface complexity. To obtain improved seismic imaging of the complex subsurface structures, near-surface modelling constrained by near-surface survey information and a series of multi domain noise suppression algorithms targeting specific noise types have been applied achieving good results. However, in response to the ongoing issues of low signal-to-noise ratio and high uncertainty in imaging caused by complex surface and subsurface structures, we consider it important to evaluate approaches to data regularization and interpolation. In this paper we compare and discuss the effectiveness of several different strategies for seismic data regularization and interpolation based on matching pursuit Fourier interpolation (MPFI) and time domain radon interpolation (TDRI). Furthermore, we conclude that in areas such as the Qaidam basin which exhibit both surface and subsurface complexity, the radial domain 5D MPFI can effectively improve signal-to-noise ratio, enhance the continuity of subsurface reflection events, and provide regular sampling at each azimuth direction, which is beneficial for subsequent structural interpretation and azimuth anisotropy analysis.
A Developed Robust Model and Artificial Intelligence Techniques to Predict Drilling Fluid Density and Equivalent Circulation Density in Real-Time
Authors: Al-Rubaii Mohammed Murif 
Published: February 2024

Abstract
When drilling deep wells, it is important to regulate the formation pressure and prevent kicks. This is achieved by controlling the equivalent circulation density (ECD), which becomes crucial in high-pressure and high-temperature wells. ECD is particularly important in formations where the pore pressure and fracture pressure are close from each other (narrow windows). However, the current methods for measuring ECD using downhole sensors can be expensive and limited by operational constraints such as high pressure and temperature. Therefore, to overcome this challenge, two novel models name as ECDeffc.m and MWeffc.m with approach was developed to predict ECD and mud weight (MW) from surface drilling parameters, including standpipe pressure, rate of penetration, drill string rotation, and mud properties. In addition, by utilizing an artificial neural network (ANN) and a support vector machine (SVM), ECD was estimated with a correlation co-efficient of 0.9947 and an average absolute percentage error of 0.23%. Meanwhile, a decision tree (DT) was employed to estimate MW with a correlation coefficient of 0.9353 and an average absolute percentage error of 0.001%. The two novel models were compared with the artificial intelligence (AI) techniques to evaluate the developed models. The results proved that the two novel models were more accurate with the value that obtained from pressure while drilling tools (PWD). These models can be utilized during well design and while drilling operation to evaluate and monitor the appropriate mud weight and equivalent circulation density for saving time and money by eliminating the need for expensive downhole equipment and commercial software.
Challenges of Laminated Shaly Rocks Evaluation and Fit-for-Purpose Core Analysis Workflow
Authors: Al-Ofi S. , Ma S. , Al-Hammad R. , Zhang J. 
Published: February 2024

AbstractEvaluation of thinly laminated shaly sand rocks in the laboratory is challenging due to rock heterogeneity and its complex minerology, for fine scale heterogeneities may result in discrepancies of results obtained from different core analysis methods. In this laboratory study, we address those challenges and present a technique to properly evaluate laminated shaly rock properties for more accurate formation evaluation.Reservoir cores were cut from a US study Beta well. Lithologies are clean and shaly sandstones. Porosity was measured at dry condition with a gas porosimeter. Effects of drying temperature and time on porosity measurements were systematically investigated. The samples were then saturated with brine and porosity was re-measured using a bench top NMR machine. Results of both laboratory measurements were compared and integrated with log interpretations for better understanding of the formation.Data obtained in this study show that the measured gas porosity is sensitive to drying conditions for core samples of laminated shaly sands. Comparing measurements of gas porosimetry and NMR indicate that some clay and capillary bound water are difficult to remove even at temperature as high as 300 Deg C, while the NMR porosity showed consistent trend with total porosity derived from logs. While total porosity from cores across the cleaner sand and moderately laminated zones showed good agreement with total porosity from logs, microfractures which are often invisible did affect the quality of core porosity measurements, thus core-log integration.In summary, although porosity is a fundamental rock property, accuracy and representativeness of porosity measurement may not be trivial, especially for laminated shaly rocks which are geologically more complex, mechanically more sensitive to stress, and thus more difficult to handle and prepare from coring to core analysis. As the most commonly used porosity measurement technique, gas porosimeter may not be the best method for porosity measurement of laminated shaly rocks because of difficulties in sample drying, and also cleaning. In this case, NMR porosity may be a better choice. In addition, because of the stress sensitiveness of laminated core samples, measurements under formation stress will ensure core analysis data more in situ representativeness.
A New Downhole Heating Technology Study and Pilot to Improve Steam Quality
Authors: Yang Lilong , Sun Shouguo , Wu Xiaoming , Yuan Aiwu , Zhang Fuxing , Zhao Chao , Sun Zhenyu 
Published: February 2024

Abstrct
This article introduces an unconventional downhole heating technology to improve steam quality. We put a long heater installed in coiled tubing consuming electric energy to the well to heat the steam injected from the wellhead to reservoir to increase the steam quality whose depth up to 1600m or larger, so the reservoir need high quality steam can be developed.
The most important part of this technology is the downhole heater. Big power is the first character and it must be easy to operate, so it is difficult to produce. After many times failures, the heater is ultimately produced successfully by pre-installing special electric wire in coiled tube with insulation around them and then drawing the coiled tube in special machine. We call it "monolithic-draw" technology. With this technology, the produced heater can produce enough energy to improve steam quality downhole and it works safely. Matched pull-push equipment and intelligent power adjust module are also developed.
The heater is produced first time and its length is about 200 meters and the power is about 400kW. The diameter is about 33mm and we test the insulation ability and heating speed in lab. After complicated designed test, we get the relationship between steam quality, power and injection speed, so we can adjust it to adequate power to satisfy field need. Field pilot shows it can heat about 20000m3/h air injected into the well to 480°C and heat low quality steam with 50% dryness to higher quality steam at the depth up to1600m- 2500m. At the depth, now with this technology we can get 50% dryness steam at well bottom by injecting steam from wellhead, so many previous undeveloped super heavy reservoirs can be developed economically. Field pilot was conducted in well group 68, the well group production is increased from 33t/d to 69t/d because of the improvement of steam quality and the operation cost is reduced by 41% because there is no heat loss.
This technology is new developed and it benefits to reduce carbon dioxide discharge because the heater can use green electricity made by wind or sun. It will be widely used in deep buried heavy oil reservoir, and it may be a revolutionary steam producer in comparison with conventional steam producer.