bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine Learning Based Prediction of Porosity and Water Saturation from Varg Field Reservoir Well Logs
Authors: Andersen Pål Østebø , Skjeldal Miranda , Augustsson Carita 
Published: June 2022

Abstract
Accurate estimation of reservoir parameters such as fluid saturations and porosity is important for assessing petroleum volumes, economics and decisionmaking. Such parameters are derived from interpretation of petrophysical logs or time-consuming, expensive core analyses. Not all wells are cored in a field, and the number of fully cored wells is limited. In this study, a time-efficient and economical method to estimate porosity, water saturation and hydrocarbon saturation is employed. Two Least Squares Support Vector Machine (LSSVM) machine learning models, optimized with Particle Swarm Optimization (PSO), were developed to predict these reservoir parameters, respectively.
The models were developed based on data from five wells in the Varg field, Central North Sea, Norway where the data were randomized and split into an unseen fraction (10%) and a fraction used to train the models (90%). In addition to the unseen fraction, a sixth well from the Varg field was used to assess the models.
The samples are mainly sandstone with different contents of shale, while fluids water, oil and gas were present. The ‘seen’ data were randomized into calibration, validation and testing sets during the model development. The petrophysical logs in the study were Gamma-ray, Self-potential, Acoustic, Neutron porosity, bulk density, caliper, deep resistivity, and medium resistivity. The log based inputs were made more linear (via log operations) when relevant and normalized to be more comparable in the algorithms. Feature selection was conducted to identify the most relevant petrophysical logs and remove those that are considered less relevant. Three and four of the eight logs were sufficient, to reach optimum performance of porosity and saturation prediction, respectively. Porosity was predicted with R2 = 0.79 and 0.70 on the model development set and unseen set, for saturation it was 0.71 and 0.61, a similar performance as on the training and testing sets at the development stage. The R2 was close to zero on the new well, although the predicted values were physical and within the observed data scatter range as the model development set. Possible improvements were identified in dataset preparation and feature selection to get more robust models.
Density Changes at Supercritical and Near-Critical Conditions by Increasing CO2 Content in Synthetic Hydrocarbon Mixtures – A Comparison Between Experiments and Simulation Predictions
Authors: Aslanidis Panagiotis , Marinakis Dimitris , Puntervold Tina , Gaganis Vasilis , Varotsis Nikolaos 
Published: June 2022

Abstract
Carbon dioxide (CO2) injection is a well-known EOR-method to reduce residual oil in the pore network of oil reservoirs. It is also increasingly used as a means of mitigating the greenhouse gas emissions problem by storing it in geological formations. A key parameter to such attempts is the density of the rich CO2 mixture, which is formed downhole in the injection well, since it affects the swelling potential, oil formation volume factor, viscosity, hydrostatic gradient, fluid distribution and formation pore pressure. The density of the crude oil-CO2 mixture depends on the pressure-temperature conditions, the CO2 concentration and the dominant hydrogen compounds in the crude oil, i.e. whether they are aliphatics, aromatics, or naphtenics (cyclic structures). The PVT properties of the different CO2-hydrocarbon mixtures vary greatly and the available experimental data for tuning PVT simulators are scarce, especially for ternary mixtures at high pressures and CO2 concentrations.
This study investigates the effect of CO2 concentration on the density of ternary mixtures containing CO2, methane, and a pure liquid hydrocarbon, which is either an alkane, aromatic or cycloalkane compound. The liquid hydrocarbons used in the study were normal heptane (n-C7), toluene (Tol) and cyclohexane (c-C6). The measurements were conducted at variable compositions, at temperatures of 50, 70, and 90 °C, and at pressures ranging between 100 and 517 bar. The ternary mixtures were:
Methane, toluene and CO2 at 1:1 molar ratio and CO2 concentrations of 14%, 27% and 72%, Methane, cyclohexane and CO2 at 1:1 molar ratio and CO2 concentrations of 19%, 47% and 68%. Methane, n-heptane and CO2 at constant molar hydrocarbon ratio (C1/n-C7) of 2:1 and varying CO2 concentrations of 23% and 75%,
Some of the rich CO2 mixtures exhibited retrograde condensation behaviour at high temperatures. The results were compared against predictions from an EoS model (Peng Robinson Equation of State), coupled with volume shift parameters. The comparison between the simulation calculations and the experimental data indicated good agreement in the densities, but significant deviations in the boiling point pressures (Pb). As a result, the EoS model can be safely used to predict the CO2 mass storage potential of reservoirs of known pore volume such as the depleted ones.
Improved Initialization of Non-Linear Solvers in Numerical Simulation of Flow in Porous Media with a Deep Learning Approach
Authors: Abbasi Jassem , Andersen Pål Østebø 
Published: June 2022

Abstract
Partial Differential Equations (PDEs) have a wide list of applications in modeling complex processes including flow in porous materials. Solution of these equations that are mostly highly non-linear is generally possible using numerical algorithms that are carried out by iterative approaches like Newton's method, where the calculations to find the solution at a new time step are started using an initial guess of the unknown variables. The computational efficiency of the calculations is highly dependent on the closeness of these initial guesses to the exact values. As a routine, solvers pick the solutions at the previous timestep as the kickoff point for Newton's method. Improvement of this starting point at each time step can reduce the time-to-solution of the solver. This study focuses on using a Deep Learning (DL) algorithm for optimization of a PDE solver and improvement of the computational efficiency of simulation of flow in porous media by providing more efficient initial guesses for the unknown variables.
In this work, a 1D gravity-capillary driven two-phase flow problem with a fully implicit Newton's solver was hired as the base numerical model. A fully connected neural network (NN) was initialized and added to the numerical solver, at the point before starting each timestep. The data from the most important features and the target properties were collected from a series of simulation cases and the DL model was trained with the Adam optimizer. After training and testing, the default initialization approach (i.e., solution at previous timestep) was replaced by the hybrid DL-based approach that provides an initial guess for cells with high saturation gradients. Saturation gradients at the previous timestep, location, and mobility ratio of both phases are selected as the input features. The results showed that applying the developed algorithm to numerical simulation problems reduced the run-time in the range of 15-35% in different cases, while the required processing time of the DL model was only around 1-3% of the whole simulation. The model performed acceptably when the effective parameters are like porosity, permeability and capillary pressure deviated from the range of the training data in order of 100%. The model performance declined when this deviation increased. This hybrid initialization approach showed the possibility of applying DL methodologies for the improvement of the numerical simulation processes.
Early- and Late-Time Prediction of Counter-Current Spontaneous Imbibition and Estimation of the Capillary Diffusion Coefficient
Authors: Andersen Pål Østebø 
Published: June 2022

Abstract
Solutions are presented to predict 1D counter-current spontaneous imbibition oil recovery at early and late times, i.e. before and after the imbibing water reaches the noflow boundary based on knowing the capillary diffusion coefficient. The system is presented in a normalized form where the space, time and saturation variables are scaled. The normalized capillary diffusion coefficient (NCDC) has an area of 1 when integrated over the saturation range with positive capillary pressure (where spontaneous imbibition occurs). The scaled solution of the system hence only depends on the shape of the NCDC function and not its magnitude (which by definition has an area of 1).
Based on the semi-analytical solution by McWhorter and Sunada (1990) scaled recovery equals the square root of scaled time for early times. The time scale depends on one part τ related to known constants, and a part Tch related to the NCDC shape. The normalized critical time Tn,cr when the square root regime ends is known exactly from this solution. Recovery thereafter depends on the NCDC and must be calculated numerically.
A dataset is generated based on combining 1000 sets of relative permeabilities and capillary pressure functions with mixed-wet to strongly water-wet states and end point mobility ratios into corresponding NCDCs. Recovery is studied in terms of how long the early square root of time behavior lasts, what the imbibition rate coefficient is in that period, and how recovery changes at late time. The former lasts longer than Tn,cr in practice and is instead characterized by a transition time Tn,tr. The parameters Tch, Tn,tr are well correlated with the CDNC shape, as quantified by one or two fractions zα,β denoting the fraction of the NCDC between normalized saturations α and β. Late time recovery was modeled using an extended Arps type decline curve where one parameter r was correlated with zα,β. We thus find relations between the shape of the NCDC and the behavior of the solution at early and late times.
Next we interpret recovery data to estimate the absolute capillary diffusion coefficient. This is done by systematically determining RFtr and the Arps parameter from tuning or type curves and linking them to associated NCDC fractions zα,β. The methods are illustrated using literature experimental data. The predicted NCDCs could predict the original recovery curves. It was found that systems with high non-wetting phase mobility and strong water-wetness are likely to have most of the recovery occur as proportional to the square root of time.
Integrated Field Development Modelling to Improve Recovery from a Complex Fractured Carbonate Reservoir with Potential for Low Salinity Waterflooding EOR
Authors: Al-Subaihi Meshari , Al-Rashidi Tahani , Prasad Raj Kishore , Dutta Dipankar , Kshirsagar Atul , Patil Tushar , Nagarkoti Malvika , Burns Christopher , Cranfield Colin Bertrand 
Published: June 2022

Abstract
Integrated field development studies were performed to increase oil recovery from the Marrat reservoir in the Umm Gudair field, a large, low permeability, complex, naturally fractured and highly faulted carbonate reservoir. The studies involved rebuilding the static model, creating and history matching a new dynamic model and using it to examine redevelopment scenarios. These included well interventions and workovers under primary depletion, secondary waterflood and, following a screening exercise, low salinity flooding (LSF).
A new structural interpretation of 3D seismic data provided a revised static geological model and yielded insight into the number, geometry and origin of the many faults intersecting the reservoir. Rock types defined from core analysis were distributed in the static geological model using trends from Bayesian lithofacies classification based on pre-stack inversion of seismic data. Porosity and permeability were modelled by rock type. Saturation-height functions for each rock type were developed from mercury injection capillary pressure (MICP) data; and the reservoir free water level was varied so that these functions honoured the log-based water saturation interpretation.
The dynamic model input description was based on available and interpreted data for the assumed oil wet reservoir. The history matching was aided by sophisticated application of decline curve analysis (DCA) and used an Opportunity Index approach to optimise well placement. The history matching led to a simplified and effective solution for characterising the locally naturally fractured reservoir nature. The effect of high permeabilities associated with increased fracture density was accommodated by introducing facies-based and distance from fault-related permeability modifiers, while maintaining geological rigour.
The dynamic model was used to examine a range of field redevelopment scenarios. This showed that LSF could enhance field recovery and achieve a three-fold increase in estimated ultimate recovery, in conjunction with other improved reservoir management strategies. The results provided support for specialised laboratory and dynamic modelling investigations as a precursor to LSF pilot trials. A low cost source of LSF injectant was identified which could contribute to lowering the overall carbon footprint.
Carbonated Smart Water Injection for Enhanced Oil Recovery in Sandstone Reservoirs of Upper Assam Basin, India
Authors: Vadhan Ramanpreet Singh , Phukan Ranjan 
Published: June 2022

Abstract
Smart water injection (SWI) and carbonate water injection (CWI) have been successfully used in enhanced oil recovery (EOR) due to favorable crude oil-brine-rock interactions. In this study, these two EOR methods are combined as one hybrid EOR process namely carbonated smart water injection (CSWI). An attempt was made to study the EOR potential in sandstone reservoirs of Upper Assam Basin in India. The synergetic effects in CSIR arises due to analogy between CO2 solubility in brine and composition/concentration of brine. First, characterization of reservoir rock and fluids collected from major producing oilfields of Upper Assam Basin. Second, optimal smart water for the candidate reservoirs determined by measuring the contact angles and oil-water IFT at different compositions and concentrations of formation water. Third, preparation of carbonated smart water and effect on reservoir rock wettability. Fourth, screening optimal carbonated smart water solutions. Fifth, applicability of the CSWI method as an EOR technique in sandstone reservoirs investigated using lab-scale core flooding experiments and the results compared with the oil recovery by SWI and CWI methods. The analyses also marked the crude as medium gravity, acidic and suitable for alkaline flooding. The formation water analysis showed the presence of different types of dissolved cations and anions with total salinity of 9534 ppm. From the original oil-wet (ϴ = 115°) state of the core, wettability alteration to intermediate-wet (ϴ = 79°) conditions were achieved with smart water. The IFT experiments demonstrated the EOR potential of smart water as the oil-water was reduced by more than half from the initial 22.18 to 7.59 mN/m. The use of carbonated water on the core surface changed core wettability to a water-wet because there was a reduction in the pH of the aqueous phase, and this modified the charges on the oil/water, and water/rock interfaces, and hence the wettability of the system. Optimal CSWI formulations screened based on contact angle and IFT measurements were used for tertiary flooding resulted in incremental oil recovery of approximately 14.44 % oil in place. This recovery was sufficiently higher than the HSB and LSB flood recoveries of 35.98% and 45.38% respectively. Thus, the core flooding highlighted the EOR potential of CSWI in sandstone reservoirs. The combined CSWI process when applied to sandstone reservoirs increases the efficiency of wettability alteration due to the ability of carbonated smart water to modify the crude oil-brine-rock interactions. Thus, both CWI and SWI can be effectively combined and engineered to achieve improved oil recovery in sandstone reservoirs. Moreover, during CSWI processCO2 moves from the brine into the oil phase, which altered the reservoir rock characteristics and the physical properties of reservoir fluids.
Integrated Geo-Dynamic Modelling and Investigation of Pressure-Dependent Permeability to Increase Working Gas Capacity of a Reservoir Repurposed for Underground Gas Storage
Authors: Qu Lin , Yu Huijuan , Mao Chuanqin , Salter Timothy Mark , Ning Fei , Van Der Kroef Roderick Floris Matthijs , Cranfield Colin Bertrand , Kshirsagar Atul , Li Limin , Randazzo Santi 
Published: June 2022

Abstract
Natural gas consumption will grow significantly in coming decades in response to cleaner energy initiatives. Underground gas storage (UGS) will be key to addressing short term supply and demand dynamics during this energy transition.
This paper presents a study on the XiangGuoSi (XGS) fractured carbonate, gas reservoir onshore China which had been converted to UGS. The focus is on how integrated studies around a shared subsurface model including coupled simulation can be used to maximise working gas storage capacity and hence increase deliverability to meet future peak gas demand. Critically, the robust integration of this study raised confidence sufficiently to propose that reservoir pressure during future gas storage cycles could be increased above the original (pre-production) pressure.
Reducing Carbon Footprint of Matrix Acidizing in Carbonate Formations. How Much Acid Do We Really Need to Pump?
Authors: Ali Mahmoud T. 
Published: June 2022

Abstract
With uncertain global economic conditions and calls for lowering carbon footprint, optimizing carbonate matrix acidizing along with other operations is crucial for operators and services companies. Accurate modeling of wormhole growth is inevitable to optimize acid placement in the field. In this study, a field-scale carbonate acidizing model tuned and validated against 600+ linear and radial experiments is presented to optimize/improve field treatments.
Unlike previous semi-empirical models, this model presents the wormhole velocity as a function of Darcy's velocity. This model was validated against a huge number of linear HCl experiments with various temperatures, acid concentrations, rock types, and core dimensions. Both internal and published data were used. The upscaling from linear to radial flow was based on changing the flow area and the number of wormholes as a function of acid progression in the formation. The upscaled model was validated against radial experiments and field observations. The results show that the model can accurately predict acid performance under field conditions.
The results of this model have revealed that limestone and dolomite formations can be stimulated using HCl acid volumes as low as 50 gal/ft. For limestone formations, low volume acidizing operations (50 gal/ft.) can result in skin as low as −3.1 (well-flow efficiency (WFE) of 1.6). On the other hand, moderate-high volume acidizing operations (100 gal/ft.) will result in skin of −3.7 (WFE of 1.8). The effect of temperature on the performance increases as the volume of the injected acid increased. At very low injection rates (long horizontals/ low Kh zones), pumping 100 gal/ft. will create 5 ft. long and 2 ft. long wormhole at 100°F and 300°F, respectively.
For the dolomite formations, pumping 50 gal/ft. of 15% HCl at 150°F will result in skin of −2.7 (WFE of 1.5), while pumping 100 gal/ft. will result in skin of −3.1 (WFE of 1.6). At 75°F, the injection of 100 gal/ft. will create short wormhole (2.8 ft.) in dolomite and long wormhole (12.6) in limestone. This is because the acidizing process is reaction rate limited at low temperature dolomite.
This paper presents a new carbonate matrix acidizing model to optimize carbonate acidizing field treatments. Carbonate wells can be stimulated using low acid volumes with little effect on well performance. That will reduce both the cost and the carbon footprint of those operations.
Decarbonizing Thermal Enhanced Oil Recovery Operations Through Improvements in Saturated Steam Distribution System
Authors: Al-Fazari Mohamed Salim , AL-Hinai Abdullah Khalifa , AL-Ghefeili Hilal Mohammed 
Published: June 2022

Abstract
Petroleum Development Oman LLC (PDO) is Oman's premier oil producer and operates several fields at "A". "A" East and "A" West are located on the eastern flank of the South Oman Salt Basin. As "A" thermal fields contain heavy oil, PDO plans to significantly increase oil recovery by increasing steam injection, which has high energy intensity.
The objective of this paper is to demonstrate that the improvement in thermal EOR operation by initiating decarbonization initiatives resulted in a significant reduction in Green House Gases (GHG) emissions.
Operational comparison of steam generation unit cost to produce one tonnage of steam for different combinations found that OTSGs is the most expensive combination as it is associated with higher taxation cost (i.e. 90% of total cost is from carbon tax). OT-HRSGs with Miraah Solar combination is found one of the best to reduce the overall unit cost and maximize the production.
The Miraah Solar facility was built at "A" to cater for the steam growth requirement. The adaptation of Miraah shall reduce the annual gas usage related to steam production by utilizing the sun's radiation. This facility is one of the largest solar plants of its kind in the world. It has an energy production capacity of 330 MWe of peak thermal energy and daily steam output of 1,980 tons per day.
An Energy Assessment (EA) had has been conducted for top energy consumers (e.g. OT-HRSGs, OTSGs, export pumps, etc.) to identify the top high GHG emitters and feature opportunities to reduce their carbon footprint. Miraah Solar enhancement was one of the identified opportunities that will result in GHG saving more than 100,000 TCO2e/yr. In 2021, a 41% improvement is seen in terms of production and GHG reduction.
In addition, the Thermal Steam Integration System (SIS) is an integrated and novel system developed to allow all steam generators to be integrated into a system that will smooth the operation and enhance the availability of the steam system/network in both on-plot and off-plot.. This will allow us to reduce GHG emissions from steam systems by 80,000 tons CO2/year by 2030, as well as reduce power consumption by more than 36% from the base case.