bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analytical Investigation of the Confinement Effect on Capillary Condensation Pressure of Fluids in Nanopores
Authors: Yang Gang , Chai Di , Fan Zhaoqi , Li Xiaoli 
Published: April 2019

Abstract
Phase behavior in nanopores, because of its widespread industrial applications, has attracted great attention from numerous researchers in recent years. However, there are still several aspects that are not fully understood in this area, such as the confinement effect on saturation pressure, critical properties, and phase boundary.
In this work, real gas effect is firstly incorporated into the original Kelvin equation, along with the pore size effect on surface tension, the multilayer adsorption, and the molecule-wall interaction potential to improve the accuracy of the capillary condensation pressure calculation. Firstly, the validity of the original Kelvin equation is examined by calculating the capillary condensation pressure of pure fluids at different pore sizes. Subsequently, the compressibility factor is computed with a modified Peng-Robinson equation of state and the molecule-wall interaction potential is correlated with the number of adsorption layers in nanopores, both of which are coupled into the original Kelvin equation. Consequently, the modified Kelvin equation is validated with collected experimental data from the literature.
It is found that the capillary condensation pressure computed by the original Kelvin equation manifest a significant deviation from those measured from experiments at pore size smaller than 15 nm. At pore size of 2 nm, the capillary condesantion pressure calculated with the original Kelvin equation is 627.5% and 407.8% higher than the experimental data for nitrogen and argon, respectively. Among the four factors which are incorporated into the original Kelvin equation for modification, molecule-wall interaction potential has the most significant contribution with a ratio of 72%, while the pore size effect on surface tension has the least to none contribution. The modified Kelvin equation is satisfactory to calculate the suppressed capillary condensation pressure of fluids in nanopores with pore size down to 2 nm. The overall relative devation for 3 data points of nitrogen at 77 K and 5 data points for argon at 87 K is 6.5% and 6.7%, respectively.
This work provides a quantitative understanding of the confinement effect on capillary condensation pressure of fluids, which can help to investigate the phase behavior of reservoir fluids in nano-sized pores more accurately. The newly developed explicitly analytical model can be easily introduced into various simulators to provide a reliable instruction for the development of shale reservoirs.
Evaluation of Hydrophobic Proppant Transport by Aqueous Energized Fluids, Impact of Gas Volume Fraction and Proppant Size: A Holistic Approach
Authors: Pruvot Antoine , McAndrew James , Cisternas Pablo , Quintero Harvey , Wang Chuanzhong , Cherniwchan Denny , O’Neil Bill 
Published: April 2019

Abstract
Hydrophobically coated sand, when combined with gas injection into an appropriate fluid, provides a significant improvement in proppant transport compared to uncoated sand. This supports the expectation that gas bubbles adhering to sand particles improve transport properties. In addition, the increase of nitrogen gas volume fraction within the fracturing fluid significantly enhances proppant suspension, particularly for smaller size proppant. Proppant transport occurs by two mechanisms, suspended transport and bed transport.
This study aims at determining the optimum conditions and composition for a fracturing fluid, comprised of a gas dispersed into an aqueous liquid (but not as a foam), to transport hydrophobically coated proppant. The aqueous phase is comprised of water with a friction reducer, to enhance suspension performance. The impacts of the gas volume fraction of the fluid and of the proppant particle size are studied.
The experimental setup is designed to compare the proppant transport efficiency of different gaseous and/or liquid fracturing fluids. In the present case, high pressure nitrogen (13.8 MPa - 2000 psi) is dispersed within an aqueous fluid. Proppant is then injected at the entrance of a straight pipe section. This central section is equipped with several collectors where the sand can accumulate. After a specific time, the system is shut down and depressurized. Sand collection along the section provides a quantitative measurement of the proppant transport efficiency. Additionally, direct observation through sight glasses allows qualitative assessment of the transport method.
The main impact of increasing the volume fraction of gas is to increase suspended transport, but we also observed a moderate improvement in bed transport. Thus, overall we observed a significant improvement in proppant transport as the gas volume fraction was increased from 10% to 30%. This effect was greatest at the smallest particle size tested, i.e. 40/70 U.S. mesh sand, but was also observed with 30/50 and 20/40 U.S. mesh sands. The upper limit on gas volume fraction was imposed by the nature of the aqueous fluid, which is not designed to operate as a foam. Phase separation effects become dominant at high gas volume fractions. The effect of particle size is attributed to the increasing number of particles present at constant loading by mass.
Here we present data to support the selection of gas volume fraction for use with hydrophobically coated proppant, in order to optimize proppant transport in practical fracturing operations. These data are based directly on proppant transport measurement under high pressure conditions, which enable proper consideration of elasticity and proppant suspension effects of added gas.
A Newly Developed Approach to Evaluate Rock Brittleness and Fracability for Hydraulic Fracturing Optimization in Shale Gas
Authors: Salah Mohamed , Ibrahim Mohamed Mohamed , Ibrahim Mazher , Pieprzica Chester 
Published: April 2019

Abstract
To economically develop the unconventional reservoirs, it is essential to understand the petrophysical and reservoir property variations throughout the entire field in helping to identify sweet spot areas and guide both completion and hydraulic fracturing design.
The brittle shales are more likely to be naturally fractured responding well to hydraulic fracturing. Ductile shales, on the contrary, are more plastic, absorb energy, and are not considered good producers. In such shales, the formation tends to heal any natural or induced fractures. Thus, formation sections with high brittleness are considered good candidates for hydraulic fracturing. However, many researchers argue that this viewpoint is not reasonable, because rock brittleness is not an indicator of rock strength and the current brittleness indices are based on elastic modulus or mineralogy. Brittle rock just has shorter plastic deformation, and it is not certain that it is easier to fracture brittle rock than ductile rock since brittle formation may have greater strength than ductile formation. So, rock fracability term is introduced to correct the shortcoming of rock brittleness. Fracability is defined as the rock failure under the ultimate rock strength in either brittle or ductile formation. The higher the fracability of the formation, the smaller formation strength. Therefore, the good hydraulic fracture candidate is the formation that does not only have high brittleness but also high fracability thus, less energy is required to induce fracture.
A fracability model integrating the rock elastic properties, fracture toughness and confining pressure is presented in this paper. Geomechanical rock properties derived from analysis of full-wave sonic logs and core samples were combined to develop sophisticated models to verify the principle of brittleness and fracability indices and to screen hydraulic fracturing candidate intervals.
Predictive Maintenance for Rod Pumps
Authors: Bangert Patrick , Sharaf Sayed 
Published: April 2019

Abstract
Approximately 20% of all oilwells in the world use a beam pump to raise crude oil to the surface. The proper maintenance of these pumps is thus an important issue in oilfield operations. We wish to know, preferably before the failure, what is wrong with the pump. Maintenance issues on the downhole part of a beam pump can be reliably diagnosed from a plot of the displacement and load on the traveling valve; a diagram known as a dynamometer card. We demonstrate in this paper that this analysis can be fully automated using machine learning techniques that teach themselves to recognize various classes of damage in advance of the failure. We use a dataset of of 35292 sample cards drawn from 299 beam pumps in the Bahrain oilfield. We can detect 11 different damage classes from each other and from the normal class with an accuracy of 99.9%. This high accuracy makes it possible to automatically diagnose beam pumps in real-time and for the maintenance crew to focus on fixing pumps instead of monitoring them, which increases overall oil yield and decreases environmental impact.
Drilling Fluid Impact on Injectivity During Multistage Fracturing in Horizontal Wells
Authors: Asel Sultan , Al Moajil Abdullah , Al-Harbi Bader , Al Darweesh Sajjad 
Published: April 2019

Abstract
Multistage Fracturing (MSF) completions have been extensively used as an economical intervention means to handle extreme downhole environment. To have adequate Injectivity during hydraulic fracturing (HF), sand face permeability should be exposed to minimum formation damage. Impaired injectivity is caused by a mechanical obstruction across wellbore, tight formations (low permeability) and high heterogeneity or invaded particulates by Drill-In-Fluid (DIF) that damaged the formation. Many causes were suspected such as formation heterogeneity, false indication of completion port opening, and damage to the sand face due to drilling filter cake. Solvent treatments, acid wash and tubular slotting are the most reasonable remedies to this issue, adding more cost to the operating company. The objective of this study is to present technical procedures and methodologies followed to investigate impaired injectivity associated with MSF completions. Results and recommendations provide remedial recipes and optimized drilling and production operating procedures. Therefore, realizing a significant cost saving for operating companies.
X-Ray Diffraction (XRD), X-Ray Fluorescence (XRF), and Environmental Scanning Electron Microscope (ESEM) techniques were used to analyze solid samples. HP/HT aging cells were used to conduct solubility testing of filter cake and typical barite/oil-based sludge sample. HP/HT filter press was used to study the effect of time on filter cake build-up at 300°F.
HCl acid showed low solubility for typical oil-based drilling fluid sludge at 310°F. Mutual solvents dissolved the sludge partially at 300°F (e.g. ~40wt% solubility). DTPA and EDTA-based chelating agents showed higher dissolution power compared to the HCl solutions. The addition of mutual solvent to the tested chemicals increased the solubility of the sludge. Among tested chemicals, 90 % DTPA and 5 % mutual solvent provided the higher solubility. High filtration rate was noticed during HP/HT filtration test. The thickness of the filter cake increased significantly as a consequence of filtration/soaking time (e.g., up to a week). Therefore, to avoid filter cake build up and the problems associated with that, the filter cake should be removed immediately after the drilling operations.
Evaluation of Fractured Rocks through Anomalous Diffusion
Authors: Raghavan Rajagopal , Chen Chih-Cheng 
Published: April 2019

Abstract
A minimal mathematical model for transient diffusion in fractured rocks at the well scale with options to link topological and geometrical properties of fractured rocks where the geology is complex is presented. We simulate transient diffusion in systems with complex geology through a transient, interporosity model consisting of three constants to consider both complex structures that may exist at very fine scales and forms that result in long-range connectivity across multiple links in a network. Fractional constitutive flux laws that address super- and subdiffusive influences and result in transient propagation of the form r2~ta, with a≠1, are considered. The spatiotemporal evolution of the transient is documented, and well-codified Flow Regimes and sub-Flow Regimes are cataloged into stages with associated typical characteristics often visible in both short- (buildup) and long-time (flow) well-behaviors. We arrive at interpretations well beyond the ‘linear-flow-regime’ that is the norm. Because the conceptual interpretation is based on a physics-based model, the ideas we present are both scalable and reproducible. We recover classical formulations through a subset of the model considered here. New behaviors, yet to be reported, are discussed.
Artificial Neural Network Modeling of Cyclic Steam Injection Process in Naturally Fractured Reservoirs
Authors: Ersahin Ahmet , Gul Sercan , Ertekin Turgay , Temizel Cenk 
Published: April 2019

Abstract
Introducing heat into a heavy oil reservoir is a well-known enhanced oil recovery (EOR) technique. Cyclic steam injection (CSI), also called as huff and puff, is one of the most common methods used to heat the reservoir to reduce fluid viscosity. Natural fractures provide effective connections throughout the reservoir, which make naturally fractured reservoirs great candidates for steam injection. Numerical simulators are capable of designing CSI operations in dual-porosity systems. However, the use of commercial simulator can be time-consuming when a large number of cases are examined for optimizations. Artificial neural networks (ANNs) work well to solve and classify the non-linear relationships between input and output parameters.
In this paper, one forward and two inverse ANN models are proposed to discuss the performance of CSI in naturally fractured heavy oil reservoirs and to discuss smart proxy models’ mimicking ability of the commonly used numerical models. In this study, we focus on a single injection and soaking cycle for a single well. The first stage of the production starts with initial recovery, and it continues until the oil flow rate drops down to a predetermined threshold value, then, the second stage (the injection phase) takes place. Injection phase is followed by the soaking phase. Delivery of steam creates a heat chamber around the wellbore (stimulated zone) where the reservoir temperature dramatically increases. The non-uniform temperature distribution of the reservoir ends up encountering different viscosity values of the oil.
The forward ANN model successfully forecasts both cumulative oil production profiles and viscosity changes around the wellbore. Reservoir properties, rock and fluid interaction parameters, fluid properties, and injection design parameters (such as steam quality, injection rate, injection time, etc.) were used as input features. A variety of ANN architectures were tested for the minimum testing errors.
The first inverse-looking ANN model, Inverse Model 1, was designed for determining the ideal injection design parameters for a desired cumulative production profile. Once injection design parameters were selected, the forward ANN model was run for those injection design parameters for verification purposes.
Second inverse-looking ANN model, Inverse Model 2, characterizes significant reservoir parameters including; matrix porosity, matrix permeability, fracture permeability, and fracture spacing. It can be laboriously challenging and computationally costly to obtain these parameters for some fields. This ANN model is trained with injection design parameters and resulting performance indicators. All the ANN-based models are controlled by a user-friendly graphical interface for the ease of the user.
Real-Time Steam Allocation Workflow Using Machine Learning for Digital Heavy Oil Reservoirs
Authors: Sibaweihi N. , Patel R. G. , Guevara J. L. , Gates I. D. , Trivedi J. J. 
Published: April 2019

Abstract
Thermal oil recovery processes are widely used to extract bitumen and heavy oil. Traditionally, a predetermined amount of steam is allocated to various injector wells using reservoir model based open-loop optimization. This practice can face a number of constraints including interruptions in well operations and/or surface facilities. Given that steam supply costs are a significant contributor to the overall production cost of heavy oil, dynamic and intelligent allocation of steam to various wells in the oilfield deserves further attention.
In this study, we propose a proactive steam allocation workflow that can learn the effect of steam injection pattern on heavy oil recovery by using machine learning. We employ data analytic predictive models for the short-term forecast of the key performance indicators (KPIs). Model parameters are updated continuously by using a moving horizon approach that considers selected prior data including real-time measurements. An objective function containing predicted KPIs is maximized by manipulating the amount of steam allocated to various injectors in the oilfield. The workflow is repeated on a daily basis for continuous optimum steam allocation.
A case study is performed by using a 3D reservoir model that represents a segment of the steam-assisted gravity drainage (SAGD) operation. For each well, the polynomial model is identified in the time-domain to forecast KPIs. The effectiveness of the proposed method is evident from the results as NPV is increased by almost 25% – 50% compared to the base case with a constant steam injection pattern in all cases studied. Due to the efficient use of available steam, the steam-to-oil ratio is reduced significantly. An adaptive and flexible steam supply is also honored by the proposed workflow, ensuring maximum efficiency of the oil recovery process. Practical implications of the proposed intelligent steam allocation workflow will be consequential in improving the operational efficiency of the digital heavy oil assets, thereby increasing profits and reducing the carbon footprint.
In-Situ Combustion Frontal Stability Analysis
Authors: Zhu Zhouyuan , Liu Yanni , Liu Canhua , Wang Yuhao , Kovscek Anthony Robert 
Published: April 2019

Abstract
Due to the complex chemical reactions and multi-phase flow physics, the displacement front stability for in-situ combustion (ISC) oil recovery processes is not well understood. In this work, we present the theory and numerical simulation for establishing analytical front stability criteria for ISC processes.
We first analyze the four influencing factors for thermal displacement stability: viscous force, heat conduction, matrix permeability changes, and gravity. A thorough analysis of the different zones and displacement fronts in a typical ISC process is conducted, with the most unstable front identified. Second, we establish the analytical solutions for judging the frontal stability. Third, numerical reservoir simulation is performed to study the frontal stability/instability and also to validate the analytical theory. We have carefully selected differential schemes, spatial and temporal discretization to ensure the accuracy of these simulations.
We have identified four major zones and three displacement fronts (reaction zone, leading edge of steam plateau, and oil bank leading edge) in a typical 1D ISC process. The most unstable front with the largest pressure gradient contrast is the leading edge of steam plateau. By establishing material and energy balance and solving the wavy perturbation of the steam front, we obtain the analytical equation for deciding the ISC flood frontal stability. In numerical simulations, we are able to obtain results with enough accuracy to capture unstable ISC displacements and show fingering behavior in different conditions. We have found matrix permeability reduction due to coke deposition has minimal impact on frontal stability. The simulation results are successfully validated with the analytical work for conditions where the ISC process is stable or unstable, which demonstrates its predictive capability for frontal stability.
In conclusion, we have established a theoretical framework to analyze at certain conditions whether the displacement of an ISC process is stable or not. Numerical simulations confirm its predictive capability. It serves as a new reservoir engineering tool for the implementation and design of practical ISC projects.
Improvement of Reservoir Management Efficiency Using Stochastic Capacitance Resistance Model
Authors: Kim Tae Hyung 
Published: April 2019

Abstract
Lost Hills is a dual permeability Diatomite reservoir that is distinct from conventional reservoirs. Application of numerical simulation has been limited throughout field life due to the complex nature of the diatomite including low permeability (∼ 1 md) but high porosity (∼ 50%) and weak rock strength (∼ 100,000 psi of Young's modulus). Thus, many reservoir management practices are based on trial and error methods which are sub-optimal. This work aims to enhance the efficiency of reservoir management activities.
The usage of Capacitance Resistance Model (CRM) has been increasing due to its simple but effective capabilities of analyzing waterflooding performances. However, CRM has innate limitations as well. The core calculation of CRM is solving nonlinear regression. Solution of nonlinear regression is not unique since it is not guaranteed to find the global minimum and is affected by solver algorithms and initial guesses. In addition to the innate limitations, due to the lack of bottomhole pressure data in the Lost Hills and its high oil viscosity (∼ 20 °API), the accuracy of Lost Hills CRM solution is not enough to be used in daily operations. Stochastic CRM (SCRM) was developed to mitigate these limitations by combining bootstrap with CRM and provides stochastic answers. SCRM estimates probabilities of an initial solution using bootstrap, substitutes low probable parameter values with P50 values, and updates injector-producer connection pairs and its interwell connectivity.
SCRM was developed for analyzing waterflooding operations such as identification of ineffective injector-producer connection pairs and estimation of reservoir pressure. SCRM analysis results were benchmarked against the Lost Hills tracer test data and demonstrated that SCRM provided a better solution than CRM. Compared to the fact that the deterministic solution from CRM found only 50% of connection pairs which tracer identified, SCRM solution identified 10 out of the 12 tracer identified connections. After the verification, SCRM was applied to find out connected injectors which cause Fluid Over Pump (FOP) wells. The existing workflow for identifying connected injectors was a trial and error method and hard to find connected injectors if connected injectors are located farther than 300 ft from FOP wells (chronic FOP wells). The novel workflow has been deployed so that FOP wells can be mitigated systematically and enable the optimization team to improve its reservoir management efficiency. In 2017, 10 chronic FOP wells were mitigated by identifying connected injectors with the novel workflow.