bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluating the Effects of Acid Fracture Etching Patterns on Conductivity Estimation Using Machine Learning Techniques
Authors: Desouky Mahmoud , Aljawad Murtada Saleh , Alhoori Hamed , Al-Shehri Dhafer 
Published: June 2020

Abstract
The successful design of an acid fracture job requires accurate prediction of fractured well productivity. Productivity estimation demands knowledge of both the acid penetration length and conductivity distribution for the given reservoir. The literature includes several models developed to predict the conductivity of acid fractured rock. The most popular is empirical and based on measuring the conductivity of 25 acid fracture experiments. The present research provides empirical models utilizing machine learning techniques and incorporating 97 experiments and 563 datapoints.
We conducted an extensive literature review to collect the published data on acid fracture experiments. The objective of such experiments is to measure conductivity at different formation closure stresses while considering field conditions. We used several data preprocessing techniques to clean the data, fill in missing values, exclude outliers and failed experiments, and standardize the dataset. Regularization was employed to eliminate features that didn't contribute to accurate prediction. Feature engineering was used to construct new features from our dataset. We began by measuring the correlations between features to better understand the data. We then built various machine learning models to predict acid fracture conductivity.
It has been observed that developing one universal empirical correlation often results in significant errors in conductivity estimation because different rock types result in different etching patterns that cannot be explained by a single correlation. For instance, the channeling etching pattern is mostly observed in limestone formations, while a roughness pattern is seen in dolomite and chalk rock. Moreover, the conductivities of etching patterns formed in chalk, dolomite, and limestone formations behave differently. We built machine learning classification techniques to determine the most likely etching patterns (e.g., channeling, roughness). A linear regression-based model was then developed as a baseline for comparison with other models generated through ridge regression. We evaluated the performances of our models using well-known metrics such as accuracy, precision, recall, mean squared error, and correlation coefficients. We also employed cross-validation to avoid over-fitting, finding that certain features were the most important in predicting acid fracture conductivity.
Detailed empirical conductivity correlations and models were developed in this work for three carbonate rock types. Previously, a single empirical model has often been employed to estimate acid fracture conductivity or, at best, a model has been developed for a particular rock type. Most models have not considered the impact of etching patterns on conductivity, which was found to be significant in limestone.
Performance Evaluation of Long-Horizontal-Well Solvent-Assisted SAGD in Rising Stage
Authors: Liu Hao , Wang Yuan , Cheng Linsong , Huang Shijun , Chen Xiao 
Published: June 2020

Abstract
Solvent-Assisted Steam-Assisted Gravity Drainage (SA-SAGD) is invented by adding solvent into the injection stream. However, the high cost of the solvent makes it necessary to ensure a high efficiency of solvent transportation from the long-horizontal wellbore to the chamber edge. In this study, a novel model considering the wellbore/chamber coupling effects is established, which can accurately evaluate the performance of SA-SAGD.
First, governing equations for mass flow in the wellbore are built on the basis of the mass and momentum balance determined by coupling the wellbore and steam chamber. Besides, implicit equations for phase changes were derived according to the heat-mass transfer between wellbore and chamber. Second, the model for oil flow rate in the chamber rising stage is derived by the chamber height identified by the variation in thermo-physical properties of solvent and steam in the chamber along the wellbore. Third, by applying Levenberg-Marquardt Algorithm (LMA), the mathematical model is solved
The results show that: the oil rate of classic SAGD is significantly improved by the solvent of C5, while the improvement effect of C7 is quite small. As for C3-SAGD, the oil rate is even lower than that of traditional SAGD. This is mainly because C3 decreases the chamber temperature and the solubility of C3 in heavy oil is low, which results in a relatively higher oil viscosity at the chamber edge compared to other solvents. The differentials between the cases, which consider the wellbore or not, is different for different solvent injection. Specifically, oil rate differential for C3-SAGD is the highest and it is twice that for conventional SAGD which has the least differential. C5-SAGD has the second-largest differential which is followed by C7-SAGD.
Currently, applying SA-SAGD in a field encounters many challenges, especially in understanding its mechanisms and improving economic efficiencies such as a conflict between the production increase and solvent cost. The newly proposed model not only sheds light on the key mechanism of wellbore/chamber coupling effects, which is usually neglected in the literature but also brings greater benefits by better designing the future SA-SAGD heavy oil recovery projects.
Carbon Dioxide Sequestration in Unconventional Shale Reservoirs Via Physical Adsorption: An Experimental Investigation
Authors: Fakher Sherif , El-Tonbary Ahmed , Abdelaal Hesham , Elgahawy Youssef , Imqam Abdulmohsin 
Published: June 2020

Abstract
Carbon dioxide (CO2) is the main greenhouse gas contributing to environmental damage and global warming. It is emitted as a result of many processes, part of which is combustion of oil and gas. One of the methods by which CO2 emissions can be controlled or reduced is through CO2 sequestration processes. This research investigates the ability to store CO2 in shale reservoirs through adsorption and some of the factors impacting the adsorption capacity. CO2 adsorption was measured using the volumetric adsorption method using pulverized shale particles of uniform size. Initially, the void space in the shale-bearing cell was measured using helium. The void space is used in the CO2 adsorption calculations in order to account for the extra volume created when the shale core was pulverized. The effect of varying the CO2 pressure, temperature, and shale volume on the CO2 adsorption capacity was studied. Results showed that both pressure and temperature had a strong effect of CO2 adsorption, with an increase in pressure resulting in an increase in adsorption and an increase in temperature resulting in a decrease in adsorption. Altering the volume of the shale resulted in a change in adsorption as well due to an increase in error as the shale volume decreased relative to the vessel volume. This research provides insight on the impact of multiple factors on CO2 adsorption to shale particles thus illustrating the potential for CO2 storage in unconventional shale reservoirs.
Numerical Modelling the Stress Dependent Transitional Fluid Flow in Coal Bed Methane Reservoirs
Authors: Nainar Subhashini , Govindarajan Suresh Kumar 
Published: June 2020

Abstract
The effects of the coupled reservoir and fluid properties, in addition to the geomechanical effects in the reservoir, are studied on reservoir pressure and cumulative gas produced. The behavioral variation of reservoir pressure, rock properties, and fluid properties are, numerically, studied both spatially and temporally.
The coal formation, subjected to dewatering and subsequent phases, is analyzed. The study of single-phase water followed by the multiphase flow of gas and water, in the transient region, is carried out. The drained distance dynamically varies with time and expands away from the wellbore. The flow to the wellbore is either a single-phase flow or a combination of single and multiphase flow. The transition between phases modeled to maintain continuity between the flow phases. Cleat and fluid compressibility are varied with time to analyze their individual and coupled effect on flow parameters.
The study details flow pattern at a time and how it changes when the time expands. The multiphase nonlinear advective term affects the flow pattern, which was, initially, diffusive in single-phase. Pressure, at any point in the reservoir, is revised when the flow regime and phases altered. The cumulative gas produced at the end of each phase and the cleat permeability, due to coupled reservoir compaction and matrix shrinkage effects, of the reservoir, in comparison with the original, is estimated. It can be observed, from the results of the study, that the flow pattern remains the same for a phase type with changing time. The flow pattern changes once the phase change from single to multiphase and vice versa.
The study is novel from previous works because the cumulative production is not a function of the constant, but varying, reservoir and fluid property that helps to understand the pressure distribution in the reservoir for every phase at each time step and especially at the transitional zones. The study is beneficial to the industry in analyzing the different fluid phase flow patterns and flow associated parameters, which ultimately, after single-phase gas flow, will give us an idea of the recovery factor of methane gas and the change in reservoir petrophysical parameters from the original state.
Impact of Digitalization on the Way of Working and Skills Development in Hydrocarbon Production Forecasting and Project Decision Analysis
Authors: Clemens Torsten , Viechtbauer-Gruber Margit 
Published: June 2020

Abstract
Digitalization and Artificial Intelligence have impacted the oil and gas industry. Drilling of wells, predictive maintenance and digital fields are examples for the use of these technologies. In hydrocarbon production forecasting, numerical reservoir models and "digital twins" of reservoirs have been used for decades. However, increasing computing power and Artificial Intelligence recently enabled oil and gas companies to generate "digital siblings" of reservoirs (model ensembles) covering the uncertainty range in static data (e.g. petrophysics, geological structure), dynamic data (e.g. oil or gas properties) and economics (Capital Expenditures, Operating Expenditures). Machine Learning and Artificial Intelligence are applied to condition the model ensembles to measured data and improve hydrocarbon production forecasting under uncertainty.
The model ensembles can be used for quantitative decision making under uncertainty. This allows companies to shorten the time for field (re-)development planning and to develop into learning organizations for decision making.
These developments require companies to change the way of working in hydrocarbon production forecasting and decision analysis. Additional skills need to be developed in companies to embrace digitalization. Data science - which is considered a key skill in digitalization - has not been identified as crucial in skills development of oil and gas companies in the past. However, for data driven decision making, advanced data analytics skills and data science skills are a pre-requisite. To overcome this skill gap, staff needs to be trained and graduates with data science and profound physical and chemical skills need to be hired.
Furthermore, skills development has to address the challenge of incorrect use of Machine Learning technologies and the risks of Artificial Intelligence leading to erroneous optimizations. In particular interpretability of AI needs to be covered in skills development.
Compaction Seismicity: What Determines Seismic vs. Non-Seismic Behavior in Dutch Gas Fields?
Authors: de Pater C. J. , Berentsen Cas , Martens Hans 
Published: June 2020

Abstract
Reservoir compaction in depleting gas fields can cause seismicity, as has been observed in a dozen countries (Foulger et al., 2019). So, it is no surprise that some of the Dutch gas fields induce many earthquakes up to magnitude 3.5. Remarkably, the occurrence of Dutch seismicity is strongly clustered in two regions, while most fields induce no recorded seismicity at all. It has been attempted to explain this from differences in reservoir characteristics, but as yet, no reservoir property has been found that can explain the difference in seismicity. A new mechanism is proposed that can explain the regional variation of seismicity.
For a few gas reservoirs, the evolution of potential fault slippage is simulated using the commonly adopted Mohr-Coulomb failure criterion. This shows that fault criticality is expected for reservoirs that showed seismic as well as non-seismic behavior. Apparently, some characteristic property is missing to explain the difference in behavior.
Using published pressure histories for seismically active gas fields, the relation is shown between seismic magnitude and relative depletion. It appears that in many cases, the first induced earthquake is relatively strong which suggests substantial cohesion of the faults. It is plausible from the geological history that in non-seismic regions, fault cohesion is larger, so that slippage is inhibited.
Multi-Dimensional Project Based Learning on Understanding Petrophysical Properties by Utilizing Image Processing and 3D Printing
Authors: Alyafei Nayef , Bautista Jerahmeel , Mari Sahar , Khan Talha , Seers Thomas 
Published: June 2020

Abstract
We present a project-based learning prototype for visual analysis of petrophysical properties using 2D cross-sections and micro-models of porous media. Micro-computed Tomography (CT) scans are used to create the quasi-2D micro-models that are printed using Stereolithography (SLA) 3D printers to study petrophysical properties in porous media. The methodology involves obtaining 8 different cross-sections of rocks either from micro-CT scans or online libraries. 2D cross-sections are segmented into black and white binary images and then skeletonized to create quasi-2D models. The flow of oil and water in initially water saturated pores in the printed 2D models mimics the drainage and imbibition processes, respectively. High definition photography is used to capture still and dynamic photographs of flow processes. The binary images are used to analyze porosity and grain size distribution while the still and dynamic photographs are used to analyze fluid saturation and displacement efficiency. The images are analyzed using open source software where a systematic tutorial is provided. The primary outcome of this project is to improve the understanding of petrophysical concepts and 3D printing by the utilization of imagery to create porous media. This project has been tested in teaching and showed major improvements in students’ understanding of petrophysical concepts when compared to pre-project. The data and tutorials used in this project are made available for the community to use through a link in the paper.
Wavelet Modelling Approach for Reservoir Model Classification
Authors: Alimohammadi Hamzeh , Chen Shengnan , Karimi Hassan , Rahmanifard Hamid 
Published: June 2020

Abstract
In oil and gas industry it is crucial to have reliable information on well, reservoir and boundary types and properties. Detailed information can be extracted from a proper interpretation of pressure and rate transients of well testing data. Though, there are times that even with an in-depth pressure transient analysis, a unique solution on well, boundary and especially the reservoir types cannot be obtained and makes it difficult or even impossible to extract correct information. In this study deep learning (DL) is used to tackle this problem by differentiate possible reservoir models and select the most appropriate model based on pressure derivative response. Accuracy of the classification model on real field data with known models is also explored.
Reservoir models can be identified by measuring the downhole pressure data and analyzing the changes in trends in pressure curves and especially pressure derivative curves. In this study, different DL algorithms are used to identify the basic characteristics of pressure derivative curves to determine reservoir model. Several possible well/reservoir/boundary types are considered to select the best model that can be used for well/reservoir/boundary property estimation. Before feeding the networks, training data curves would be shrunk in size using wavelet transform (WT) which is able to sustain the pressure derivative features in a much-compressed form to accelerate algorithm training and testing.
The technique used in this work is a time-efficient process that learns important signatures of pressure derivative curves to classify reservoir models. Unlike the conventional well testing methods in which models are determined from the visual inspection of the pressure and pressure derivative plots, the technique used in this study was trained with a dataset consists of hundreds of reservoir models generated by solving diffusivity equation under different well, reservoir, and boundary conditions. The procedure was applied to multiple field examples with known reservoir model and reservoir properties and proved the consistency and flexibility of the methodology for true reservoir model selection. DL-based models also shown to be very handy with excellent computational efficiency especially when dealing with the complex patterns on the pressure derivative curves. The study showed that the method has great capability to classify pressure derivative and can also tolerate noise when applied on real pressure data.
Large dataset used in this study can increase the comprehensiveness of the training and test data sets. The big advantage of the DL-based approach was the improvement in the pattern recognition of the pressure derivative curves without the need of any feature handcrafting or any prior knowledge of well, reservoir, and boundary types. ML proved to be a reliable, fast, and accurate technique that can significantly improve the process of well, reservoir, and boundary type detection based on pressure derivative curves.
Multi-Dimensional Approach to Inter-Well Connectivity Analysis
Authors: Wong Lee Jean , Amini Hamed , MacBeth Colin 
Published: June 2020

Abstract
Carbonates are known to be heterogeneous. In this paper, we will focus on characterising inter-well connectivity by applying a multi-dimensional approach to production data analysis along with integration of inter-disciplinary data for a Brazillian carbonate reservoir. Results of the analysis are used for interpretation of a noisy 4D seismic data to locate sweet spots.
This unique integrated approach characterizes inter-well connectivity from four perspectives: (1) determine reservoir quality (2) identify source of water production (3) tracking of injected fluid's flow path (4) verify impact on 4D seismic response. We will show how the quality of a carbonate reservoir and its aquifer strength can be verified with well logs, pressure depletion rates and production behaviour. The use of sensitivity analysis in mechanistic models will also be shared to analyse the impact of heterogeneity on production behaviour. Using Chan's (1995) water-oil-ratio diagnostic plots, the source of water production will be identified as well. Explanation of analysis of well chronology, bubble maps of water cut will also be provided for tracking of injected fluid flow paths. Finally, interaction of production parameters between well-pairs and resistance modelling will be used to evaluate inter-well connectivity, and verified with 4D seismic data.
Findings from all the analysis in the integrated approach are summarized into an inter-well connectivity metric, which is used as a reference for production and seismic history matching and interpretation of the noisy 4D seismic data. The integrated data analysis shows that the sweet spot corresponds with softening on the 4D seismic map, un-swept by injectors as it is located on a structural high southeast of the reservoir. This paper offers a comprehensive analysis to characterize important reservoir characteristics (such as thief zones and tight streaks). It will also emphasize ways to integrate inter-disciplinary data, and showcase various visualization perspectives to fortify and enhance the importance of data integration.
Influence of Total Organic Content on CO2–Water– Sandstone Wettability and CO2 Geo-Storage Capacity
Authors: Fauziah Cut Aja , Al-Khdheeawi Emad A. , Iglauer Stefan , Barifcani Ahmed 
Published: June 2020

Abstract
Wettability of CO2–water– reservoir rock system is a key factor to determine fluid dynamic and storage capacities in CO2 geo-storage process. Despite the past researches on this matter, the parameters that influence the CO2–water–rock wettability variation are still not fully understood. One of these parameters is rock-total organic content (TOC). Thus, here, we investigated the effect of TOC on the CO2–water–sandstone wettability and the implication for CO2 geo-storage at relevant reservoir conditions. The used sandstone samples were retrieved from the South West Hub CO2 capture and storage project (GSWA Harvey 1) in Western Australia. Here, we measured the contact angles for a range of sandstone TOC (i.e. 0.01 wt %, 0.015 wt %, 0.017 wt %, and 0.019 wt % TOC) at various pressures (5 MPa, 10 MPa, 15 MPa, and 20 MPa) and at an isothermal reservoir temperature (334 K). The results indicate that both of the advancing (θa) and receding (θr) contact angles for all tested sandstones increased with an increase in pressure TOC, implying the system turned to be more CO2-wet (e.g. the advancing contact angle increased from 92° to 118° when the sandstone TOC increased from 0.01 wt % to 0.019 wt %, at 20 MPa and reservoir temperature). Furthermore, our results indicate that the sandstone contact angle increase with pressure for all tested TOC values. Thus, we conclude that minute increasing in TOC can increase the sandstone contact angle and thus decrease the residual trapping capacities. Our results suggest that the contact angle measurement of rock retrieved from reservoirs should be treated properly for TOC to have an accurate estimation for the CO2 storage capacity.