bluuebunny/crossref_metadata_2025 huggingface dataset:
Prevalencias de estado de nutrición en recién nacidos mexicanos por peso y longitud al nacimiento: un análisis de los certificados de nacimiento del Sinac
Authors: López-Rodríguez Guadalupe , Galván-García Marcos , Galván-Valencia Oscar
Published: June 2022

Objetivo. Describir la prevalencia nacional por entidad federativa del estado de nutrición de peso y longitud al nacimiento. Material y métodos. Estudio transversal descriptivo. Se analizaron datos de 1 907 341 recién nacidos vivos en 2017, registrados en el Subsistema de Información sobre Nacimientos (Sinac). Los percentiles para peso y lon­gitud se estimaron en la plataforma INTERGROWTH-21st. Resultados. La prevalencia de pequeños para la edad ges­tacional (PEG) y longitud insuficiente (LI) fue de 7.4 y 4.8%, respectivamente. Se registraron diferencias por sexo en las prevalencias de LI, PEG y grandes para la edad gestacional (GEG) (p <0.01). Las entidades con mayores prevalencias de PEG (10.4%) fueron Estado de México y Yucatán. De GEG, fueron Sonora (16.8%) y Baja California Sur (15.3%). Conclu­sión. Dimensionar el estado de nutrición al nacer permite identificar entidades que requieren acciones focalizadas para disminuir los riesgos asociados con la malnutrición.
An Integrated Model Combining Complex Fracture Networks and Time-Varying Data Modeling Techniques for Production Performance Analysis in Tight Reservoirs
Authors: Cao Chong , Cheng Linsong , Jia Zhihao , Jia Pin , Zhang Xuze , Xue Yongchao
Published: June 2022

Efficient development of tight reservoirs often relies on complex-hydraulic-fracture-network. Due to the time repeated iteration for simulation, the (semi)-analytical model or fully-numerical model often requires a trade-off especially for the accuracy of production analysis. Hence, a comprehensive model for accelerating production matching needs to be established. In this paper, a neighboring-long-short-term-memory (n-LSTM) model, integrated with a complex fracture semi-analytical flow model, can make production performance analysis with high efficiency. The interconnections between hydraulic and natural fractures with arbitrary angles and complex geometry were considered in flow model. Then, the reservoir flow derived from Laplace domain was coupled with fracture network flow numerically solved by finite difference method to obtain the semi-analytical flow solution. The specific distribution of flow solutions was obtained based on the range of reservoir properties, well information, and geological parameters. Thus datasets including production rate and date can be constructed, enlarged and split into training and testing dataset.

The integrated model proposed in this paper adopted a non-orthogonal network with 4100 feet length and 53 segments for testing, and was applied for the characterization of complex fractures in the Changqing tight reservoir in the Ordos Basin, China. It is worth mentioning that 65 semi-analytical solutions are expanded to 1280 pairs of production-time data point using the n-LSTM model. With the strong power of capture and excavate the non-linear relationship between multitype data, it only takes a few minutes to forecast and match the daily production data with samples from actual oilfield. As a result, the mean square error of 0.31% in the training dataset and 2.63% in the testing dataset shows that the semi-analytical solution that accurately characterizes the complex fracture networks can be combined with improved LSTM for the prediction and analysis of oil production. In addition, it can be found that the prediction results of the integrated model can also identify the 1/4 slope and 1/2 slope straight lines in the log/log transient response curve. The interpreted results expand the application of semi-analytical solution assisted data-driven model and reduce the consumption of a large amount of repetition time.

This paper provides an integrated data-driven model combined with semi-analytical model to make well performance analysis with more efficiency and high accuracy. This workflow, incorporated with fracture precise characterization, data generation and expansion, prediction and calibration, can be readily applied in oilfield to obtain fracture parameters with less time. In addition, time-series and small samples can be enlarged and excavated, especially for the in-proper records in production history.
Temperature Specifications for CCUS Completions Equipment: Steady-State and Transient Thermal Simulations
Authors: Vasper Adam C , Fagereng Ingvild Bakke
Published: June 2022

Different simulated injection scenarios can significantly cool carbon capture utilization and storage (CCUS)wells. Cases representing different scenarios were modeled to help determine appropriate temperature specifications for completions equipment in CCUS wells.

Depleted reservoirs, in particular gas reservoirs, will not support a static column of liquid or dense-phase CO2 to surface at the initial reservoir pressure. During injection, unless there is significant friction or backpressure, the upper part of the well will be at low pressure, with CO2 in the gas phase. Injected, liquid CO2 will depressurize across the wellhead choke, or the upper tubing, resulting in significant cooling as the saturation curve is reached and the CO2 starts flashing. For steady-state injection across a narrow range of rates, a tapered or narrow completion string can be designed that provides sufficient friction pressure to keep the CO2 in the liquid phase. A portfolio or set of wells, potentially with different tubing sizes, will give more flexibility for managing injection rates.

Transient scenarios such as injection startup or shutdown cause rapid changes in rate and can consequently induce severe thermal shocks. These cannot be completely prevented using only friction or passive downhole devices. Many completion options have been proposed to provide flexibility for injection rate changes and/or reservoir pressure increases. These include concentric or dual strings providing multiple flow paths with different flow areas and hence friction, downhole passive chokes, backpressure control valves, surface-operated downhole flow control valves, and various combinations of these and other technologies. Currently, the only methods to eliminate all the scenarios consideredmay be (active) backpressure control and surface-controlled downhole flow control valves. Whereas the transient effects are most severe in depleted reservoirs, they are also shown to occur in reservoirs approaching hydrostatic pressure.

The most important component in the completion string is arguably the subsurface safety valve (SSSV). API 14B specifies a maximum leak rate for the closed SSSV. Following a blowout or depressurization event, a leak of high-pressure, liquid CO2 from below the SSSV to low-pressure, gas-phase CO2 above will cause localized cooling at the SSSV, in addition to that caused by the initial depressurization condition. Modeling included simulations of blowout and subsequent SSSV leakage phases for different periods of injection and hence reservoir cooldown.

To quantify these effects, the work considered temperatures in the different radial sections of the well versus time and depth. Sensitivities on annulus fluid, reservoir properties including pressure and permeability, the rate of mass transfer between phases, and other variables were also investigated. Modeling was performed using a commercial dynamic multiphase flow simulator.
Model for Predicting Wellbore Temperature and Pressure in Offshore CO2 Storage Wells and the Influencing Factors
Authors: Ma Weizhen , Guan Zhichuan , Yan Yan , Li Cheng
Published: June 2022

The CO2 physical parameters are greatly influenced by the wellbore temperature and pressure during the offshore burial process, but the existing model does not take this into account resulting in poor calculation accuracy, which brings large errors to the later wellbore load calculation and wellbore integrity evaluation. Based on heat transfer and other basic theories, a wellbore temperature and pressure calculation model was established. the calculation method of CO2 physical parameters required for the model was determined, and finally the established model was solved by iterative calculations to obtain the temperature and pressure distribution of the wellbore and the change law of physical parameters, and the effect of injection parameters on the wellbore temperature and pressure was analyzed. Taking a buried well in the South China Sea as an example, the analysis found that as the well depth increased, the wellbore temperature and pressure increased approximately linearly, and constant pressure specific heat capacity, Nusselt number and convective heat transfer coefficient of CO2 fluid increased, while the density, viscosity, friction coefficient, thermal conductivity and Prandtl number decreased. An increase in injection temperature decreases the temperature gradient, but has little effect on the pressure gradient and bottomhole temperature and pressure; an increase in injection volume decreases the temperature gradient and increases the pressure gradient, and the bottomhole temperature can be effectively regulated by the injection volume; the injection pressure has a certain effect on the temperature gradient at low pressure, but has a most no effect on the pressure gradient, and the bottomhole pressure can be effectively regulated by the injection pressure.
Limitations of Commercial Software in Estimating Trapped Gas Saturation and Hysteresis in Relative Permeability
Authors: Aghabozorgi Shokoufeh , Sohrabi Mehran
Published: June 2022

Permanent storage of carbon dioxide requires accurate estimation of trapped gas saturation and reservoir storage capacity. Hydrogen and gas storage in subsurface reservoirs involves injection and withdrawal cycles, which necessitates the accurate estimation of hysteresis in relative permeability (kr) and capillary pressure (Pc) data. Cyclic injections in the form of Water Alternating Gas (WAG) injection is an effective Enhanced Oil Recovery (EOR) method for improving volumetric sweep efficiency in the reservoirs. When simulating all these processes, subsurface reservoir engineers rely on commercial software to estimate trapped gas saturation and cyclic hysteresis in kr and Pc data. However, few researchers have investigated the reliability of the available hysteresis models implemented in commercial software for this purpose.

To this aim, a thorough sensitivity analysis was performed to investigate the performance of three-phase hysteresis models available in the literature for simulating the measured data of cyclic experiments. The Water Alternating Gas (WAG) experiment used in this study was performed at near-miscible conditions on a mixed-wet sandstone sample, and the cyclic injection was started with drainage.

The results show that when simulating WAG experiments performed at low injection rates, using a hysteresis model developed by Aghabozorgi and Sohrabi (2019) provides the most accurate results for pressure drop, fluid saturations, and gradual oil production. However, the model developed by Larsen and Skauge (1998) can be only used for experiments reaching maximum gas saturation in their first three-phase imbibition cycle, mostly representing the limited grid blocks near the wellbore. Even in this scenario, the dependence of the model on Land’s initial-residual relationship and trapping coefficient is questionable.

This study highlights the possible pitfalls of using commercial software for estimating trapped saturation and hysteresis in relative permeability and capillary pressure. It also provides useful suggestions for improving the accuracy of performed simulations. The presented results are vital for the oil and gas experts as they can demand improvements in current approaches and promote constant growth and development of commercial software.
Application of Bag-of-Features Approach to Drilling Accidents Forecasting
Authors: Gurina Ekaterina , Klyuchnikov Nikita , Antipova Ksenia , Koroteev Dmitry
Published: June 2022

A significant proportion of capital and operational expenditures of oil and gas companies falls on the well construction. Unexpected situations inevitably happen during drilling regardless of the well's construction technology level and available information. These situations lead to more spending and noon-productive time. We present a machine learning (ML) algorithm for predicting accidents such as stuck, mud loss, and fluid show as the most common accidents in the industry.

The model for forecasting the drilling accidents is based on the Bag-of-features approach, which implies labeling segments of surface telemetry data by the particular symbol, named codeword, from the defined codebook. Building histograms of symbols for the one-hour telemetry interval, one could use the histogram as an input for the machine learning algorithm. For the ML model training, we use data from more than 100 drilling accidents from different oil and gas wells, where we defined more than 3000 drilling accident predecessors and about 5000 normal drilling segments.

Model performance was estimated using two major metrics.The coveragemetric, indicates the ratio of true forecasted events. Number of false alarms per day metricfor the specified probability threshold. Using different schemes of metric calculation, one could evaluate the model's ability to both forecast and detect accidents. Validation tests justify that our algorithm performs well on historical and real-time data. At each moment, the model analyzes the real-time data for the last hour and provides the probability of whether the segments contain the signs of drilling accident predecessors of a particular type. The prediction quality does not vary from field to field, so the ML model can be used in different fields without additional training.

Nowadays model is tested in real oilfields in Russia. To operate the model, we developed software integrated with the Wellsite Information Transfer Standard Markup Language (WITSML) data server into clients' existing IT infrastructure. All calculations arein the cloud anddo not require significant additional computing power on client side.
Should We Care About the Background Gas Effect on Reservoir Properties Prediction Using Machine Learning and Advanced Mud Gas Data?
Authors: Anifowose Fatai Adesina , Mezghani Mokhles Mustapha , Badawood Saleh Mohammed , Ismail Javid
Published: June 2022

Background gas is the baseline gas measurement due to the recycled gas dissolved in or expelled from the drilling mud additives. It occurs more in oil-based mud systems than in water-based. A cut-off is usually applied on the mud gas data to remove the background gas effect in traditional mud gas analyses. This imposes an overhead on modeling procedures. This study investigates the effect of applying the cut-off on the performance of machine learning algorithms.

A case of porosity prediction using advanced mud gas data is considered in this study. Using data from six wells, we implemented two experiments to compare the performance of artificial neural networks (ANN) with and without the cut-off. The first experiment applies a cut-off of 100 ppm on the total normalized gas while the second uses the entire data without the cut-off. The comparative results are benchmarked with those of a multivariate linear regression (MLR). Each well dataset was split into training and validation subsets using a randomized sampling approach in the ratio of 70:30.

The results compare each of the MLR and ANN models individually and over all the datasets without and with the cut-off applied. The ANN models show better or same performance on the datasets without the cut-off in four out of six cases (67%). This shows that the ANN models may be less affected by the presence of the background gases in the mud gas datasets. It could be preliminarily concluded, based on the data used in this study, that it might be unnecessary to apply cut-offs on the mud gas data for ML algorithms due to their capability to handle noisy data. This conclusion is, however, subject to more extensive studies while ensuring consistency. Avoiding the application of the cut-off will remove the unnecessary overhead and provide more data for effective ML model training.

While the results of this preliminary study somewhat agree with the traditional practice of applying a cut-off on advanced mud gas data, more extensive experiments will be conducted in our future work to further validate the conclusion. The background gas is traditionally considered noisy. In ML modeling, it could provide more information to further explain the nonlinear relationship between the input features and the target variable, hence improving the predictive capability.
Investigation and Simulation of SWAG injections Performed in Mixed-Wet Carbonate Rocks.
Authors: Alnuaimi Latifa Obaid , Aghabozorgi Shokoufeh , Sohrabi Mehran
Published: June 2022

The average recovery factor of current producing oil reservoirs is about 35-50% worldwide. Enhanced Oil Recovery (EOR) methods such as Water Alternating Gas (WAG) target the oil left in place and improve the final recovery of the developed fields. In a WAG injection plan, some reservoir blocks experience simultaneous gas and water flow. Therefore, Simultaneous Water And Gas (SWAG) injection experiments are performed to understand and simulate the fluid flow behaviour in these blocks more accurately.

The experimental data we analyzed in this manuscript were obtained by performing a SWAG experiment using real reservoir rock and fluid (mixed-wet carbonate rock extracted from the Abu-Dhabi field). In miscible and immiscible experiments, the injected gas was Methane and CO2, respectively. We tried to simulate the experiments using Stone's, Baker's, and Stone's exponent models to evaluate the performance of these models in simulating SWAG experiments. It was shown that SWAG displacement can be simulated using Stone's first model and changing two-phase kr data as a matching parameter. The results showed that we do not need to correct the three-phase relative permeability in the low oil saturation region for simulating SWAG experiments.

The study presented in this paper is novel in two aspects: first, the SWAG experiments were conducted in reservoir carbonate samples using real reservoir fluids; and second, even though many researchers have simulated the WAG experiments, not many have discussed the simulation of SWAG experiments. The results presented in this paper is of utmost importance for decision making, designing, and simulating CO2-EOR plans in giant Abu-Dhabi carbonate reservoirs.
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

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

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

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

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

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

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

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

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

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.
The Impact of CCUS for Improved Oil Recovery on CaCO3 Scaling Potential of Produced Fluids
Authors: Ness Giulia , Sorbie Ken Stuart , Al Mesmari Ali Hassan , Masalmeh Shehadeh
Published: June 2022

Unlike other CCUS technologies, CO2 EOR has been widely implemented at a commercial level and on an industrial scale. In CO2 EOR, CO2 can be injected on its own or alternated with water in CO2 WAG (water-alternating-gas). Both applications have a direct impact on produced fluid compositions influencing GOR, water cut, CO2 concentration and consequently Ca2+, alkalinity and pH. The variation of fluid compositions has an inevitable impact on the scaling potential of produced fluids and on the resulting level of scale formation and its mitigation strategy.

The aim of this work is to investigate the scaling potential changes for a wide range of CO2 WAG scenarios in a high salinity carbonate reservoir in the Middle East using input data from reservoir modelling simulations and running multiple sensitivity studies. The main scale formed in this reservoir is calcium carbonate (CaCO3).

The equilibrium reservoir water, the produced water chemistry profiles from downhole to stock tank and the scaling risk profiles are modelled using a commercial integrated PVT and aqueous phase software. A rigorous scale prediction procedure previously published by the authors is applied to accurately calculate scale risk trends for variable production scenarios.

As CO2 increases in the WAG cycle, reservoir pH drops but the equilibrium with CaCO3 rock causes an increase in alkalinity. This results in more CaCO3 precipitation in the production system where pressure drops and CO2 flashes off solution. Hence, these results show unequivocal detrimental impact of CO2 WAG on the calcium carbonate scaling potential of produced fluids. This leads to a need for operational and/or chemical adjustments to the scale management program when this technology is deployed.

Whilst in this field some CaCO3 scale is predicted to form downhole, but this is not a severe problem although it may need to be addressed. The separator is operated at a sufficiently high pressure that calcium carbonate is not expected to form there. Changing operating pressures and CO2 and H2S concentrations can shift some of the problem to the separator, but if this remains at high pressure there will be no scale precipitation here. However, the calcium carbonate scale will predominantly precipitate at stock tank conditions.

Implementing green technologies such CCUS is fundamental to achieving net zero goals and this work clearly shows that actions need to be taken to manage the associated CaCO3 scale problems in the produced fluids to make this application successful.
Model-Based Life-Cycle Optimization for Field Development and Management Integrated with Production Facilities
Authors: Schiozer Denis J. , Santos Susana M. , Santos Antonio Alberto S. , von Hohendorff Filho João C.
Published: June 2022

Reservoir simulation models often support decision making in the development and management of petroleum fields. The process is complex, sometimes treated subjectively, and many methods and parameterization techniques are available. When added to uncertainties, the lack of standardized procedures may yield largely suboptimal decisions. In this work, we present a comprehensive outline for model-based life-cycle production optimization problems, establishing guidelines to make the process less subjective. Based on several applications and a literature review, we established a consistent methodology by defining seven elements of the process: (1) the degree of fidelity of reservoir models; (2) objective function (single- or multi-objective, nominal or probabilistic); (3) integration between reservoir and production facilities (boundary conditions, IPM); (4) parametrization (design, control and revitalization optimization variables); (5) monitoring variables (for search space reduction); (6) optimization method, including optimizer/algorithm, search space exploration, faster-objective function estimators (coarse models, emulators, others), type of ensemble-based optimization (robust or nominal based on representative models); (7) additional improvements (value of information and flexibility). With an application on a publicly available benchmark reservoir, this work shows how a model-based life-cycle optimization process can be systematically defined. In this initial work, the focus is the field development phase and some simplifications were made due to the high computational demand, but in future works we plan to address the control and revitalization variables and reduce the number of simplifications to compare. The optimization results are analyzed to understand the evolution of the objective function and the evolution of the optimization variables. We also discuss the importance of including uncertainties in the process and we discuss future work to emphasize the difference between life-cycle (control rules) and short-term (effective control) management of equipment, as well as ways to deal with the computational intensity of the problem, such as the combined use of representative models and fast simulation models.