bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prediction of CO2 Minimum Miscibility Pressure MMP Using Machine Learning Techniques
Authors: Sinha Utkarsh , Dindoruk Birol , Soliman Mohamed 
Published: August 2020

Summary
Minimum Miscibility Pressure (MMP) is a key design parameter for gas injection projects. It is a physical parameter that is a measure of local displacement efficiency while subject to some constraints due to its definition. Also, MMP value is used to tune compositional models along with proper fluid description. In general, CO2 and Hydrocarbon gases are the most common gases used for (or screened for) gas injection processes and due to recent focus to screen for the coupling of CO2-sequestration and CO2-EOR projects. As CO2-Oil phase behavior is quite different than the hydrocarbon gas-oil phase behavior, researchers developed specialized correlations for CO2. Therefore, there is a need for a tool with expanded range capabilities for MMP for CO2 gas streams. The only known measurement technique for MMP that is coherent with its definition is the use of a Slim-Tube which also restricts the amount of data available even though there are other alternative techniques presented over the last 3 decades which all suffer from various limitations. Since correlations are inexpensive one of the inexpensive and easy ways to calculate the MMP, therefore there have been several correlations developed in past based on correlative physics [9], [18], [24], [28], [50], [80], [82] and phase behavior properties of the oil - CO2 mixture [3], [5], [44]. This paper present two separate approaches to calculating the MMP of oil during pure CO2 injection, (1) Analytical correlation where the correlation coefficients were tuned using linear SVM [39], [67] and 2) using a hybrid method (combination of random forest regression [11] and proposed correlation) which very nicely captures the dynamic behavior of CO2. The model takes the compositional analysis of oils up to heptane plus fraction, molecular weight of oil, and reservoir temperature as input parameters. Based on statistical analysis and cross-plots we showed that the performance of the final proposed method is superior to all the leading correlations [9], [18], [24], [28], [50], [80], [82] and supervised machine learning [55] methods considered in this work [10], [11], [14], [15], [39], [67]. The proposed model works for the widest spectrum of MMP from 1000 to 4900 psia which cover the entire range oils within the scope of CO2 EOR based on the screening criteria [54], [75].
Successful Field Implementation of CO2-Foam Injection for Conformance Enhancement in the EVGSAU Field in the Permian Basin
Authors: Katiyar Amit , Hassanzadeh Armin , Patil Pramod , Hand Michael , Perozo Alejandro , Pecore Doug , Kalaei Hosein , Nguyen Quoc 
Published: August 2020

Abstract
This paper presents the performance of a CO2 foam injection pilot implemented in the East Vacuum Grayburg San Andres Unit (EVGSAU) by ConocoPhillips in cooperation with The Dow Chemical Company. The pilot project focuses on a single CO2 injection pattern, consisting of one injector and eight producers, selected due to signs of early gas breakthrough and poor overall sweep efficiency.
To solve these conformance issues and increase overall pattern production performance, a new foaming surfactant with low adsorption and high gas partitioning characteristics was developed and experimentally tested at simulated reservoir conditions. A "water alternating surfactant-in-gas" injection strategy was created utilizing a history matched reservoir simulation model and an empirical foam model. This reservoir model was also utilized to better understand the dependency of surfactant concentration on foam generation and fluid diversion. Injection profile logs (IPLs) were also run, in both water and CO2 phases, prior to pilot implementation to establish baseline injection performance.
This paper will detail several performance indicators that illustrate sustained improvement in pattern injection and production after more than 15 cycles of alternating water, CO2+surfactant, and CO2-only injection. During each cycle, gas injectivity trends were calculated and compared to the baseline to monitor foam strength and performance. Four additional IPLs were run, which indicated continuous improvement in vertical sweep efficiency and ultimately resulted in uniform injection distribution between the upper and lower sections of the producing zone. Finally, the most significant result of the trial was the uplift in pattern oil production. It has averaged ~20% above the baseline production forecast throughout the entire pilot period and peaked within the first six months at ~60% above the baseline.
The success of this pilot illustrates the benefits of using a low adsorbing and CO2 soluble foaming surfactant to address reservoir conformance issues for CO2 floods. Further optimization of the pilot based on the simulation forecast is planned to improve long-term pilot economics.
Artificial Diagenesis of Carbonates: Temperature Dependent Inorganic and Organic Modifications in Reservoir Mimetic Fluids
Authors: Rao Ashit , Kumar Saravana , Annink Carla , Le-Anh Duy , B. Alotaibi Mohammed , C. Ayirala Subhash , Siretanu Igor , Duits Michel , Mugele Frieder , A. Yousef Ali 
Published: August 2020

Abstract
Within reservoirs, spatial variations related to mineralogy and fluid chemistry determine the success of improved oil recovery (IOR) techniques. However, the composition and structure of mineral-adsorbent-fluid interfaces, which fundamentally determine the initial and IOR-altered wettability of reservoir rocks as well as the displacement of crude oil (CRO), are unclear. Replicating the diagenetic alterations of carbonates, this study addresses the temperature dependence of the inorganic and organic modifications of calcite by reservoir pertinent fluids as well as its consequences on mineral wettability and reactivity.
We utilize a suite of characterization methods, such as confocal Raman, scanning electron and atomic force microscopy as well as Fourier-transform infrared spectroscopy, to investigate the modifications of carbonates on aging in formation water (FW), CRO-equilibrated FW and FW-equilibrated CRO. The microscopic modifications of carbonates present positive correlations with aging temperature and also are varied, encompassing topographical alterations, cation substitution of lattice Ca2+ ions by Mg2+ ions and the deposition of particles enriched with polyaromatic hydrocarbons (PAHs) as organic adlayers. Aging in the formation waters produce substantial reconstruction of calcite surfaces, with the formation of Mg-calcite layers at elevated temperatures. Subsequent aging in brine-equilibrated CRO produces an organic coating on calcite surfaces, which is composed of PAH-enriched particles. The organic adlayers, deposited at high temperature, produce a transition in the macroscopic contact angles towards a more ‘oil wet’ tendency. In addition, the organic adlayer presents limited permeability and serves as a diffusion barrier to the reactivity of the bound mineral, as evident from substantially reduced rates of calcite dissolution. The multilayer deposition of organic particles is attributed to an interplay between bulk and surface reactions for interfacially active constituents of CRO. With the enrichment of PAHs even observed for mineral grains within reservoir rocks, the permeability and stability of organic adlayers emerge as key factors determining the wettability of carbonates as well as the diffusion behavior of ionic and molecular species at mineral-fluid interfaces.
Results of this study are relevant to multiple aspects of reservoir development and maintenance, encompassing laboratory scale wettability and core flooding experiments, in silico models as well as the advancement of IOR strategies. The observed nano- and microscopic surface alterations of carbonates within reservoir mimetic environments facilitate our understanding of the physicochemical relations between mineralogy and fluid chemistry as well as elucidate the organization of mineral-adsorbent-fluid interfaces within reservoirs.
Integration of Capacitance Resistance Model with Reservoir Simulation
Authors: Davudov Davud , Malkov Anton , Venkatraman Ashwin 
Published: August 2020

Abstract
Capacitance resistance models (CRM) provide an analytical framework to characterize and optimize water flooding fields. There are significant challenges in adapting these methods to actual field data with many complexities. In this research, we present a novel approach to integrate CRM models with reservoir simulation to improve reservoir characterization as well as identify new injection strategies to increase oil recovery. The results of this integration are presented for a specific field with challenging geological uncertainties.
In particular, the historical production and injection data taken from water flooding field has been analyzed and separated as training and prediction part. First, inter-well connectivity (gain) values are estimated based on training part of the data which are further verified with streamline simulation results. A comparison of allocation factors between wells from the reservoir simulation model and the inter-well connectivity is presented to showcase consistency. Next, the prediction capability of the model has been verified with the testing section of data. Finally, for the next future 3 years, oil production rates obtained from CRM is further verified with results from reservoir simulation.
The large number of wells and the uncertainties associated with a producing field makes CRM modeling a challenge especially for predictions. Accordingly, initial screening and analysis of field data is an essential part for successful CRM application. After examining field data with 5 injectors and 21 producers which is subject of this study, it has been observed that only 6 wells are positively correlated with injectors which has been used as an input for CRM. Results further indicate that, with correct application of CRM, for the selected wells future oil recovery can be predicted with less than 2% differences compared to simulation results. This shows the capability and practicality of CRM. The model is next used to obtain new injection schedule to increase oil recovery. The new injection rates obtained from the model predict an increase in oil recovery by 5-10%.
The continuous recording of production and injection data presents a new opportunity to integrate analytical approaches with traditional reservoir simulation approach for effective reservoir management. The presented model that includes verification of CRM results (with streamlines as well as reservoir simulation) helps benchmark these techniques that are simple and effective. The presented approach is particularly applicable for mature fields where historical production and injection data is available so that data-driven models (CRM) can be integrated with traditional reservoir simulation models.
Compositional Simulation of Cyclic Gas Injection in Liquid-Rich Shale Reservoirs Using Existing Simulators with a Framework for Incorporating Nanopores
Authors: Bi Ran , Luo Sheng , Lutkenhaus Jodie , Nasrabadi Hadi 
Published: August 2020

Abstract
The current models for predicting the phase behavior of gas injection in shale can be highly unreliable because nanopores (with diameters less than 50 nm) form a significant pore volume in many shale formations. Conventional PVT models cannot describe the phase behavior in nanopores. Here, we present a practical framework to regenerate the PVT considering the shale nanopores effect for a more reliable compositional simulation of gas injection in shale reservoirs by using existing commercial simulators.
The pore-size distribution in shale reservoirs can be discretized into a bulk-region (fractures and macropores) and nanopores. We use a pore-size-dependent equation of state (PR-C EOS) to describe the phase behavior of the fluid for each pore. Bulk fluid characterization with laboratory PVT reports determines the bulk fluid parameters for the PR-C EOS. The confinement parameters for the PR-C EOS are from the reported database (Luo et al. 2018a). Further, multi-scale phase equilibria are calculated by minimizing the free energy. We model the multi-scale constant composition expansion and constant volume depletion with volume expansion per stage. The modeling generates multi-scale PVT (formation volume factor, saturation, etc.) for the shale reservoir, which is used to retrain the Peng–Robinson equation of state (PR EOS) by modifying the acentric factor, binary interactions, and critical temperature and pressure. The retrained PR EOS is then applied in a commercial compositional simulator to forecast gas injection improved oil recovery (IOR) in shale. We also use the updated gas saturations in the multi-scale PVT model to modify the relative permeability tables used in the compositional simulation.
We predict significantly higher gas production and lower oil production when the effect of shale nanopores on the phase behavior and updated relative permeability are considered in the compositional simulation of the primary depletion of shale reservoirs. In the gas injection improved oil recovery (IOR) stage, the cumulative oil production is enhanced with both the original and multi-scale PVT models. However, when the effect of nanopores is not considered in the compositional simulation, the increases in the cumulative oil production and cumulative gas production can be underestimated and overestimated, respectively. This can have significant consequences on the economic evaluation of the gas IOR projects in shale reservoirs.
The application of multi-scale phase equilibria in shale reservoirs is challenging in compositional simulators. Our proposed framework enables engineers to incorporate multi-scale phase equilibria from the PR-C EOS in their shale reservoir simulations. It does not require a change in the cubic equations of state in current developed commercial compositional simulators, thus preserving the efficiency of the compositional simulators.
A Novel EOR Technique for the Attic Oil in Dipping Faulted Reservoirs by Utilizing Gas Oil Countercurrent and Water Flooding Assistance
Authors: Ma Kang , Jiang Hanqiao , Dong Rencheng , Wheeler Mary , Li Junjian , Zhang Rongda 
Published: August 2020

Abstract
After long-term water flooding in dipping faulted reservoirs, attic oil is formed and located above the structurally highest wells. A novel technique that combines the Gas Oil Countercurrent (GOC) and Water Flooding Assistance (WFA) is proposed in this paper in order to recover this type of remaining oil. The basic GOC mode includes three procedures, that is, gas injection, well shut-in to form a Secondary Gas Cap (SGC), and production. For the new method GOC-WFA, the WFA is considered in the gas injection and production stage. In this paper, the basic GOC and GOC-WFA experiments are performed to elaborate the EOR mechanism and efficiency of the new method. Three experiment cycles are performed in each experiment. The experiment results illustrate that the WFA can improve the production efficiency by shortening the well shut-in time. The SGC condition is much better in GOC-WFA compared to the GOC in the same well shut-in time. The well shut-in time can be shortened by 50%. In the production stage, the SGC expansion and WFA create Double Displacement Process (DDP). The WFA can inhibit the gas channeling and lower the pressure decline rate. Besides, WFA can also displace the remaining oil located between the production and injection well that cannot be displaced by the gas cap expansion. Compared to the basic GOC, the total oil production volume is nearly doubled. Based on the production model of GOC-WFA, the sensitivity of various operational and geological parameters is analyzed by the reservoir simulation method. Then, the fault-block reservoir screening condition for GOC-WFA is established; the injection-production parameters in the gas injection and production stage are optimized. Finally, the EOR feasibility of the GOC-WFA in the fault-block reservoir is testified by the experimental and simulation results. Therefore, the GOC-WFA can effectively enhance oil recovery in the sealed mature fault-block reservoir.
Improvements on Modelling Wettability Alteration by Engineered Water Injection: Geochemical Pore-Scale Approach
Authors: Bordeaux Rego Fabio , Mehrabi Mehran , Sanaei Alireza , Sepehrnoori Kamy 
Published: August 2020

Abstract
Several laboratory experiments demonstrated that different water compositions cause rocks to change from oil- to water-wet state. Although it is a consensus that wettability alteration is the main recovery mechanism, modeling the underlying mechanism is still a major challenge. Our main goal is to improve and validate a physically based model to predict contact angles from zeta-potential measurements. We propose a new mass-action formulation for surface complexation model (SCM) that includes the energy interaction effect between two close surfaces (PS). Currently, most SCMs consider rock and oil as isolated surfaces (IS). Thus, we hypothesize that, as rock and oil surface approach each other, PS model produce a better description of electrostatic distribution. Additionally, we develop a method of determining SCM equilibrium constants to fit several zeta-potential measurements for different ion concentrations (Na+, Ca2+, Mg2+, SO42- and H+). Finally, we estimate contact angles using disjoining pressure calculations and compare them with ones reported in the literature. From a SCM set of reactions available in the literature, we validate the developed IS model against PHREEQC (a reference simulator for geochemical reactions). For the PS case, the system of equations’ solution is very close to IS approach when the interaction between surfaces are negligible (wide spacing between surfaces). Regarding zeta-potential prediction for calcite-brine system, we argue that Na+ might not be an indifferent ion as suggested previously. Our simulation results indicate that, besides the renowned potential-determining ions, sodium adsorption on calcite can play an important role in electrostatic interactions, switching surface charge polarity. Thus, we only achieve a successful fit of zeta-potential measurements when Na+ is considered in the SCM reactions. Finally, contact angle estimation using the PS model and disjoining pressure theory provide good predictions of seven different cases reported in the literature. We validate our method on a total of 66 and 163 contact angle and zeta-potential measurements, respectively. The present work is a novel approach to represent how electrostatic interactions among rock, brine and oil modify the rock surface charge and the rock wetting state.
Simulation and Thermodynamic Modeling of Solvent EOR and Bitumen Recovery in Organic-Rich Tight Reservoirs
Authors: Neshat Sajjad , Okuno Ryosuke , Pope Gary 
Published: August 2020

Abstract
This paper introduces a new approach for using solvents for enhanced oil recovery from organic-rich unconventional reservoirs. The heavy organic components in the reservoir rock, i.e. bitumen, are included in the phase behavior model using a cubic equation-of-state. The phase behavior of mixtures of methane, CO2, and dimethyl ether (DME) with reservoir hydrocarbons including bitumen was studied to better understand the interaction of each solvent with reservoir fluids including water in the case of DME. The phase behavior models were then used in an equation-of-state compositional reservoir simulator to explore the potential of each solvent to increase the oil recovery including otherwise immobile bitumen from a 3D heterogeneous reservoir.
A Data-Driven Approach to Forecasting Production with Applications to Multiple Shale Plays
Authors: Xiong Hao , Kim Changjae , Fu Jing 
Published: August 2020

Abstract
Decline curve analysis has been used as a reliable method to forecast conventional reservoir well production over the last decades. Recently, an increase in the demand for oil and gas has caused unconventional reservoirs to become a prominent source of energy. However, it is challenged if we still apply the decline curve analysis in unconventional reservoirs due to its limitations such as boundary dominated flow, constant operation condition, et al. Therefore, in this paper, two new methods are proposed using machine learning method to forecast well production in unconventional reservoirs, especially on the EOR pilot projects.
The first method is the Neural Network, which allows the analysis of large quantities of data to discover meaningful patterns and relationships. Both peak production rate and hydraulic fracture parameters are used to be the key factors. Lastly, Neural Network technology is applied to investigate the relationship between key factors and oil production rate. The second method uses the Time Series Analysis. Time Series Analysis is one of the most applied data science techniques in business and finance. Since the properties of unconventional reservoir make the production prediction more difficult, it is safe to say that Time Series Analysis can yield good results on the production rate forecast.
Field production data from over 1000 wells from different shale plays (Barnett, Bakken, Bone Springs, Eagle Ford oil, Eagle Ford gas, Fayetteville, Marcellus gas, Marcellus oil, Utica oil, and Woodford) is used to verify the feasibility of these two methods. The results indicate there is a good match between the available and predicated production data. The overall R values of Neural Network and Time Series Analysis are above 0.8, which demonstrates that Neural Network and Time Series Analysis are reliable to study the dataset and provide proper production prediction. Meanwhile, when dealing with the EOR production prediction, such as Huff-n-Puff, Time Series Analysis shows more accurate results than Neural Network.
This paper proposes a thorough analysis of the feasibility of machine learning in multiple unconventional reservoirs. Instead of repeatedly fitting the production data by decline curve analysis, it also provides a more robust way and meaning reference for the evaluation of the wells.