bluuebunny/crossref_metadata_2025 huggingface dataset:
Design and Qualification of a New Erosion Resistant Sand Control Screen
Authors: Malbrel Christophe , Blackburne Edward
Published: September 2022

The issue of screen erosion is a complex problem that doesn't lend itself very easily to modeling and computer aided design, particularly when it comes to metal mesh screens. Interactions between solid and liquid (settling/suspension) and between the solids and the screen material (plugging) are complex and evolving over time due to wear and fluctuations associated with multiphase flow or other reservoir related changes over the life of the well. As a result, screen development is best performed using pilot testing to simulate downhole conditions and optimize the design. In that regard, the setting of standard performance tests is essential.

A series of time lapse erosion tests performed on mesh screens recently highlighted the benefits of shielding the screen from the basepipe perforations to improve erosion resistance. This new feature provided several fold improvements in the mesh screen erosion resistance and was implemented in a novel screen design. It consists in placing a partially perforated inner shroud underneath a regular screen cartridge, with blind spots precisely located over the basepipe holes to prevent direct line of sight flow and reducing local velocity by diffusing flow across the entire screen area.

An extended continuous erosion test was used to validate the design and qualify metal meshes, and mechanical testing as per the new API19ss standard for sand control screens was performed to qualify the new screen and demonstrate its reliability.

Comparing the performance of the new screen design against similarly built screens confirmed that the addition of the new diffusion shroud does not adversely impact the mechanical performance of the screen while imparting improved erosion resistance to the screen.
Laboratory Investigation of Chemical Tracers vs. DNA Tracers
Authors: Asadi Mahmoud , Blair Tyler , Comstock Sarah
Published: September 2022

A new and robust tracer technology is introduced based on encapsulated Nano-sized synthetic DNA. This cutting-edge technology enables bonding of synthetic DNA strands with unique sequences to a magnetic core particle and encapsulating them with silica making it possible to have unlimited number of identifiable tracers, each with a unique signature. Each manufactured batch of DNA tracer is then coated with a special chemical to make the batch water-wet or oil-wet. The presented novel technology of encapsulated Nano-sized DNA tracers is shown to be superior to the currently used water chemical tracers, fluorobenzoic acid or FBA, in many ways both in the applications of EOR and flowback analyses in hydraulic fracturing. Unlike the chemical tracers, the DNA tracers don't partition, don't chemically react with the formation minerology, don't disintegrate with time, are stable at high reservoir temperatures and don't lag flood front velocity if used in secondary recovery projects such as waterflooding. In addition, unlike the available limited number of chemical tracers, there are unlimited number of identifiable DNA tracers. In waterflooding, the DNA tracers are used to characterize fluid flow accurately and precisely in a reservoir and to identify heterogeneity of the reservoir. The technology can also be used to evaluate flowback analyses in hydraulic fracturing to fully understand fracture behavior, pipeline leakage identification, groundwater movement, contamination tracking in water streams, etc.
Transient Analysis of Sandface and Wellbore Temperature in Naturally Fractured Geothermal Reservoirs: Numerical and Analytical Approaches
Authors: Wei Cao , Cheng Shiqing , Shi Dengke , Liu Dawei , Liu Xiuwei , Gao Ruilian , Wang Yang , Yu Haiyang
Published: September 2022

This work presents the numerical and analytical temperature solutions that couple the transient reservoir/wellbore thermal models to analyze the temperature measurements recorded at producing horizon or at a certain gauge depth above it for estimating the physical and thermal properties of naturally fractured geothermal reservoir (NFGR) during drawdown and buildup periods. The NFGR is replicated by a more general triple-porosity system adopted from Abdassah and Ershaghi model. We first develop numerical and analytical solutions to predict the sandface temperature in NFGRs, accounting for the Joule-Thomson (J-T) effect, adiabatic heat expansion/compression effect, heat convection and conduction. The developed numerical solution is verified and found to agree with the proposed analytical solutions. Then, the wellbore heat flow model adopted from Hasan and Kabir model is coupled with NFGR model to predict the wellbore temperature at a certain gauge depth above producing horizon.

The results show that three heat radial flow regimes and two thermal inter-porosity regimes have been identified. It is demonstrated that the early-time heat flow is dominated by the adiabatic heat expansion/compression effect, and the intermediate- and late-time heat flows are dominated by J-T heating/cooling effect. It is demonstrated that thermal properties such as J-T coefficient, adiabatic heat expansion coefficient and fracture intrinsic porosity can be estimated through temperature transient analysis, which are not accessible by pressure data. The results also show that when the gauge location is near the producing horizon, the heat transfer regimes keep almost identical to sandface during drawdown period. As the distance between the gauge location and producing horizon increases, drawdown-wellbore temperatures are more dominated by wellbore heat loss, thereby may miss some information contents from reservoir. Moreover, buildup-wellbore temperatures are usually dominated by wellbore heat loss and do not exhibit any discernable flow regimes even though gauge location is near the producing horizon. It is demonstrated that wellbore temperature derivative exhibits unit slope at early times for drawdown and buildup periods. Finally, we develop an integrated semilog-straight-line analysis workflow of combining sandface pressure and temperature data to estimate NFGR properties. Synthetic test example is interpreted to demonstrate the applicability of the developed workflow.
An Experimental Study on Two-Phase Downward Flow of Medium Viscosity Oil and Air
Authors: Ualiyeva Gulnur , Pereyra Eduardo , Sarica Cem
Published: September 2022

The gas-liquid downward flow is the least studied flow condition, which is critically important in predicting the flow between two platforms in offshore systems and wells for CO2 injection for CCSU applications. These processes require a reliable prediction of pressure drop and phase fraction. This paper focuses on the liquid viscosity effect on downward gas-liquid flow. Flow patterns, pressure drop, and liquid holdup are measured for downward vertical and near-vertical flow. The acquired data are compared with available mechanistic models, and the discrepancies are discussed. The 2-in ID oil/water/gas outdoor facility of The University of Tulsa Fluid Flow Project has been used for this effort. The facility is 22.72 m long and is operated with medium viscosity oil and air. Liquid viscosity is fixed at 40 and 70 cP by controlling the temperature. Superficial liquid and gas velocity ranges are 0.1 – 0.3 m/s and 0.5 – 5 m/s, respectively. The inclination angle is varied between 60 and 90° with a 10° increment. Visual observations and Capacitance Sensor (CS) signals are utilized to determine flow patterns. Facility instrumentation also allows measuring pressure gradient and liquid holdup. Two classic flow patterns observed were: stratified and annular flows. Stratified is further sub-classified as stratified wavy and stratified wavy with a thin film. Annular flow is sub-classified into falling film, liquid slip, and wavy annular. Experimental interfacial shear stress (τI) and wall-liquid shear stress (τWL) were compared to model values. Existing models and commercial simulators show very poor performance in downward flow and need further improvements.
Leveraging SME Knowledge with Big Data Multiply the Value of Both
Authors: Van Kuilenburg Robert , Handels Martijn
Published: September 2022

With Managed Pressure Drilling (MPD) getting more common, its rubber seals increasingly become a source of potential downtime during drilling operations. The common approach is to provide monitoring towards relevant stakeholders via expert consultants and elaborate reporting. This is costly and time-consuming. Next to monitoring and reporting, accurate prediction of the wear and tear on the rubber seal is essential. Within technology partnership, a real-time monitoring and proactive advisory tools have successfully developed and implemented. First, a scalable data infrastructure has implemented, unifying process data from the MPD and drilling systems into a single database. Second, a hybrid seal wear prediction model has been developed, combining a model-based approach with regression analyses. Key to the success was the feedback loop between operation and engineering. This included pre-and post-run inspections and measurements on the seals tied to the operational parameters. Thirdly, repetitive reports got generated automatically for daily operations, connections and seal wear. The advanced MPD condition monitoring solution was successfully developed by combining expertise in operations and engineering with data analytics skills. It emphasizes the fact that big data works best when it is part of a solid data framework, that takes domain knowledge and users into account.
Modeling and Forecasting Enhanced Condensate Recovery from Rich Gas-Condensate Regions in Eagle Ford
Authors: Amini Kaveh , Kazemi Hossein
Published: September 2022

The development of unconventional reservoirs is strongly affected by the reliability of the production forecast and project economics. This paper has addressed these issues by analyzing the performance of several wells using both analytical and numerical modeling. For instance, after history matching a portion of each well's production history, we forecasted the remaining portion of the well's production history using the early history-matched model parameters. Next, using mathematical models, we studied the feasibility of Enhanced Condensate Recovery (ECR) to increase condensate production and to utilize and store CO2.

This research study includes multi-phase rate transient analyses of five wells and a pressure build-up test on one of the wells to calculate the stimulated permeability of the SRV region. Moreover, decline curve analysis of 34 wells in four well-groups (based on their landing zones) indicated that the wells in three landing zones had similar ‘Oil EUR’ while wells landed in the region between Austin Chalk and Eagle Ford had the highest ‘Gas EUR’.

Our Eagle Ford compositional reservoir model consisted of three horizontal wells in the liquid-rich volatile oil region. After matching the production history of the wells, our model correctly forecasted the future performance of the wells. Furthermore, the input permeability of the SRV region in the history match was two orders of magnitude higher than the core permeability—confirming permeability enhancement resulting from hydraulic fracturing.

The history-matched compositional reservoir model was used to study cyclic (Huff-n-Puff) CO2, lean wet gas, and dry gas ECR to evaluate the potential of improved condensate recovery. Each of the three injected compositions indicated considerable enhanced condensate recovery (ECR). The enhanced condensate variability was further studied by changing cycle length, injection rate and injection pressure. The optimum CO2 enhanced condensate recovery was obtained by injecting CO2 for 60 days followed by 180 days of production. The incremental condensate production after 4 cycles increased by 19% above the primary production with a net CO2 utilization of 8550 scf per bbl of incremental condensate.
Frac Optimization by Sand Entry Distribution
Authors: Shan Youngbin , Zeng Bo , Roberts Glyn , Wu Yaoguang , Song Jie , Zhou Dan
Published: September 2022

Billions of dollars are invested into hydraulic fracturing every year. However, how to optimize frac design parameters such as stage and cluster spacing, fluid and proppant volume into each stage, number of perforations per cluster and temporary isolation is still not clear.

This paper proposes a new method to estimate the volume of proppant penetrating each perforation hole. By aid of high-resolution optical imaging technology, perforation hole size before and after frac treatment can be accurately measured and interpreted. The difference in area before and after Frac is the eroded perforation area. Eroded area represents the sand entry into a perforation. Sand Entry Distribution (SED) determines the frac efficiency. Better SED gives better frac efficiency.

Operators understand that different frac treatment volumes and different cluster versus perforation holes designs will impact the productivity. With the technology proposed in this paper, different frac designs can be measured and calculated using SED. A better SED in a cluster and/or a stage is certainly a better frac design.

The method described in this paper estimates the proppant volume penetrating into each perforation hole after frac. Furthermore, a number of statistics and comparison between clusters and stages are described in the paper to give operators a clear indication of which frac design gives better frac efficiency and more uniform distribution.
Numerical Simulation and Field Verification of the Cutting Efficiency of 3D Shaped PDC Cutters
Authors: Li Chengxi , Ke Xiaohua , Guo Jianhua , Peng Qi , Ma Chi , Yu Jiaqing , Cheng Chris
Published: September 2022

Improving well-drilling technology is increasingly important in the oil and gas industry. Among these efforts, the continuous improvement of the polycrystalline diamond compact (PDC) drill bits is particularly significant. The PDC cutter is a crucial element of a PDC drill bit and the determining factor for the bit performance. Thus, optimizing the design of PDC cutters will improve drilling efficiency and reduce operational costs. Using the finite element method, this paper aims to develop a numerical model to accurately evaluate the cutting efficiency of 3D shaped PDC cutters and, in turn, facilitate the cutter shape design optimization. In this study, we used the Drucker-Prager plastic model with a damage law to simulate the constitutive behavior of the rock. The parameters used in the Drucker-Prager model were obtained by matching the numerical rock unconfined compressive strength (UCS) test results with the actual rock UCS testing data. We simulated the rock cutting process using the developed rock model. We compared the cutting forces, normal forces, and mechanical specific energies (MSEs) of various 3D-shaped cutters with those of the flat cutter. Finally, we verified the accuracy of the numerical model with lab tests and field trials. From numerical results, we found that 3D-shaped cutters’ cutting forces, normal forces, and MSEs are significantly smaller than those of the flat cutter, and such differences become more and more pronounced as the depth of cut (DOC) increases. Specifically, the difference of MSEs between the 3Dshaped cutters and the flat cutter increases from 8% at DOC of0.5mm to 23% at DOC of 4mm. The lab results verified the accuracy of the numerical results. Through field tests, we further compared the performances of the 3D-shaped cutter PDC bits with the flat cutter PDC bit. The 3Dshaped PDC bits had increased footage by 28% and a rate of penetration (ROP) of 9% in real-life applications. In this study, we developed a field-proven rock cutting finite element model which effectively and reliably facilitated the design of 3D-shaped cutters. Compared with lab-based methods, the numerical model can predict the cutting efficiency of 3D-shaped cutters at a much lower cost. By utilizing the numerical model, we can efficiently evaluate and thus quickly optimize the performance of the design of 3D-shaped PDC cutters.
Application of Remote Sensing Techniques to Detect Methane Emissions from the Oil and Gas Sector to Assist Operators with Sustainability Efforts
Authors: Esparza Ángel , Ebbs Michael , Gauthier Jean-François
Published: September 2022

The general concept of sustainability lies in the vision that incorporates the economic, social, and environmental dimensions. The energy sector has been addressed as one of the main contributors to emissions of anthropogenic greenhouse gases. Therefore, sustainability in the oil and gas (O&G) industry is mainly associated with the advancement in environmental and social performance across the industry. Individual firms, particularly those belonging to the O&G sector, are now assessed for their environmental, social, and governance (ESG) performance and their impact on climate change. To meet the different key performance indicators (KPIs) for corporate social responsibility (CSR) and ESG, the planning, development, and operation of O&G infrastructure must be conducted in an environmentally responsible. This paper discusses how methane detection of O&G infrastructure using remote sensing technologies enables operators to detect, quantify, and minimize the emissions while gaining insights and understanding of their operations via data analytics products. The remote sensing platforms accounted are satellite and aerial operating in tandem with data analytics to support sustainability initiatives and ESG metrics. This paper presents examples of measurements at O&G sites taken with GHGSat's satellites and aircraft platforms, showing evidence of methane emissions. A discussion of each platform and how they work together is presented. In addition, this paper discusses how these data can be used to achieve sustainability goals and tools for ESG initiatives through analytical models.
Use of Horizontal Drilling for CO2 Sequestration in Low Storage Capacity Aquifers: from Impossible to Feasible
Authors: Panchal Yashesh , Amirlatifi Amin , Sameh Omar , Mostafa Mahmoud , Ovalle Adriana , Abou-Sayed Omar , Mohamed Ibrahim Mohamed
Published: September 2022

The United States Department of Energy (DOE) Carbon Storage Assurance Facility Enterprise (CarbonSAFE) focuses on developing geological storage sites that can accommodate more than 50 million metric tons of Carbon Dioxide (CO2) over 25 years period. Few formations can accept this volume of CO2 through one classic vertical injection well. Multiple injection wells are usually needed to handle the targeted CO2 volume, with well spacing of several miles to avoid any pressure interference between the injectors.

Nebraska is among the largest ethanol-producing states in the USA, with 25 ethanol plants that produce more than 17 million metric tons of ethanol per year. These plants produce a significant volume of CO2 as a typical ethanol plant produces around 150,000 metric tons of CO2 annually. Several techniques have been proposed to capture and sequestrate the emitted CO2, including mineral carbonation and carbon geological storage. Among these techniques, carbon geological storage is the most feasible option, especially sequestration in deep saline aquifers because of the larger volume that can be stored underground, and lower cost compared to the other techniques.

Most of the ethanol plants are located on the eastern side of the state, while geological evaluation suggests that thick aquifers that can handle the large volume of CO2 are located in the southwest area of the state. Due to the high cost of building more than 100 miles of pipeline to transport the CO2 from the source to the injection point (pipeline costs around one million dollars per mile), thin aquifers have been identified locally near the plants to receive the generated CO2 volume. However, conducting CO2 injection operations through multiple scattered wells will increase the anticipated cost, including pore space rights, well drilling cost, land acquisition, CO2 transportation between sites, multiple injection systems and high-pressure pumps, labor, and injection monitoring.

Drilling horizontal wells can maximize the volume of CO2 that can be injected in a single well at lower injection pressure than a vertical well. The long horizontal section will expose a larger formation volume and increase the surface area available for CO2 to flow through.

St. Peter formation has been identified as one of the thin candidate formations to inject CO2 in the eastern part of Nebraska. The injection modeling conducted in this study shows that a single horizontal well with a lateral of 2,000 to 3,000 ft can replace at least three classic vertical injection wells.
Successful Application of an End-to-End Advanced Workflow for Reservoir Stimulation: Case Studies from a High-Temperature Gas Formation in Ukraine
Authors: Abdrazakov Dmitriy , Eswein Edmund , Witek Jakub , Agee Daniel , Bruce Brian , Dorokhov Maksym , Trokhymets Dmytro , Prokhorenko Stanislav
Published: September 2022

An end-to-end workflow was developed for optimization of a carbonate stimulation treatment in a high-temperature environment. The workflow includes advanced core testing, treatment fluid and design considerations, and production simulations. A case history validated the workflow and production results.

A well in central Ukraine was selected by an operator as a candidate well for a production stimulation feasibility study. The reservoir and well information were reviewed, and a reservoir model was built in an advanced matrix stimulation simulator. Core flow tests with 3D wormhole geometry visualization were used to calibrate the simulator's fluid-rock interaction parameters. Various skin and permeability profiles were subjected to iterations in the simulator with different fluids and treatment schedules. Optimum fluids were chosen according to the risk analysis, and key design objectives were considered based on the drilling records, mud type, workover history, and petrophysics.

Analysis of the drilling history revealed the necessity of using special stimulation fluids for removing damage from the oil-based mud. Analysis of the well and reservoir conditions showed that the use of nonmodified, emulsified, and organic acids was not recommended. Instead, a single-phase retarded stimulation fluid was chosen as the main reactive agent. Use of chelates was declined due to higher cost-to-efficiency ratio in comparison with the single-phase retarded stimulation fluid. The core flow tests along with 3D wormhole geometry visualization allowed optimizing the treatment parameters with respect to wormholing efficiency. The core flow test data were used to calibrate the matrix stimulation simulator with representative fluid-rock interaction curves. Due to interval length and heterogeneity, the use of diverters was recommended to increase wellbore coverage during the treatment. A polymer-free viscoelastic surfactant-based diverter was selected to alter the injection profile. An optimum treatment schedule developed in the matrix simulator included cleanout by coiled tubing equipped with a high-velocity jet, a low-rate coiled tubing matrix treatment, and a high-rate bullhead treatment. The designed treatment was successfully pumped. The post-stimulation production showed a significant increase in productivity index, without issues in cleanup. Similar workflow stimulation treatments were deployed on five subsequent wells, which have also shown very positive production response.

This work provides validation that a sound and rigorous engineering approach with advanced modeling and novel chemistry solutions can revive and significantly increase productivity of carbonate reservoirs. It was the first application of such a workflow and described stimulation fluid technology in Ukraine and Europe.
Assessing the Underground Hydrogen Storage Potential of Depleted Gas Fields in Northern California
Authors: Okoroafor Esuru Rita , Kim Tae Wook , Nazari Negar , Watkins Hannah Yuh , Saltzer Sarah D. , Kovscek Anthony R.
Published: September 2022

The goal of this study was to assess the potential for storing hydrogen underground in depleted gas fields in Northern California. We considered the potential amount of hydrogen generated from the electrolysis of California's curtailed solar and wind energy. We then determined the fields with the best geological and reservoir properties to support secure underground hydrogen storage.

We developed a three-stage set of criteria for selecting potential hydrogen storage sites. In stage 1, our screening approach combines integrated geoscience and environmental factors to identify the fields to exclude from consideration for hydrogen storage. In stage 2, we applied a numerical simulation-based site selection criteria to the fields that passed the stage 1 screening criteria. We started the screening with 182 depleted and underground storage fields in Northern California, of which 147 fields were disqualified in the first stage. We scored and ranked the remaining 35 fields based on their potential to maximize storage and withdrawal of hydrogen using the numerical simulation-based site selection criteria. The top-ten high scoring sites for underground hydrogen storage and production were reservoirs with dips between 5° and 15°, reservoir porosity above 20%, reservoir flow capacity above 5000 mDm, and reservoirs at depths between 430 m to 2400 m. The total estimated hydrogen storage capacity for the ten high-scoring sites was 203.5 million tonnes of hydrogen. Our set of site selection criteria has a stage 3 that requires detailed site characterization. With stage 3, we gather additional rock and fluid properties of high-scoring sites that enable detailed modeling of the processes related to hydrogen storage and withdrawal. We did not cover stage 3 in this paper.

We estimated the potential hydrogen recovery from a hypothetical depleted field in California and evaluated the efficiency of converting the renewable energy to hydrogen and back to power. The results show that depleted gas fields in Northern California have sufficient storage capacity to support the seasonal underground storage of hydrogen derived from renewable energy electrolysis. However, recovery is limited to the amount of fluid that can be injected, the mixing between hydrogen and the in-situ gas, and the lateral spread of hydrogen. The round-trip efficiency of power to hydrogen to power conversion maxed at 36% for the system under study.
Viscosity Estimation of Liquid Hydrocarbon Mixtures: An Application to Solvent-Based Enhanced Oil Recovery
Authors: Kaminsky Robert , MacIsaac Gordon
Published: September 2022

Various mixing rules exist to estimate the viscosity of liquid hydrocarbon mixtures given the viscosities of the individual components. These models generally work well for mixtures of components of similar viscosity but can produce quite inaccurate estimates for mixtures of light and heavy components. Enhanced oil recovery processes for heavy-oil reservoirs utilize light hydrocarbon solvents as an injectant to enhance the oil recovery. The mixture viscosity is a fundamental parameter for modelling solvent-based recovery techniques using reservoir simulation. In the present work, a large database of liquid mixture viscosity literature data has been constructed and used to test a diverse set of mixing rules. In addition to the general formulation, the impact of the component basis (i.e. the use of mole, mass, or volume fractions) was examined. The database includes a wide range of mixtures, which range in viscosity from 0.1 to about 100,000 cp. A power law mixing rule using pure component volume fractions calculated at standard conditions is shown to be, in general, very effective. Additionally, it was found that a modified Arrhenius model could provide similar accuracy as the proposed formulation but is somewhat more complicated apply.
Improved Operational Carbon Footprint Reduction by Using a Data-Driven Method in Chemical Treatment Candidate Selection
Authors: Yao Ke-Thia , Raghavenda Cauligi S. , Ashayeri Cyrus , Trivedi Dhruvil , Patel Tirth , Lal Manish , Row Richard , Aksehirli Attila , Ershaghi Iraj
Published: September 2022

This paper presents a physics based and data driven machine learning approach for chemical treatment candidate well selection in fields producing heavy oil. In heavy oil fields, cyclic steam is often used to not only to stimulate the formation around a wellbore but also to open the casing perforations or liner slots plugged by asphaltene deposits. These asphaltene and paraffin build ups can partially or fully block the passages to fluid production. Such cyclic steam jobs are done when there is significant decline in production as measured with usually a threshold over 60 days of operation. But generating steam and using it in cyclic steam jobs is costly and contribute to the carbon footprint. An alternative solution is to employ chemical treatment of wells to clean up blockages that may occur in formations (Brownlee and Zern, 1997). When there is true blockage due to deposits by heavier oil components, cleaning the perforations or liner slots is expected to improve flow and increase the production. However, historical data show that not all chemical treatment jobs yield the same level of success. The problem addressed in this paper is using a data-driven predictive modeling approach to make recommendations for candidate well selection using chemical treatment based on past performance of chemically treated wells. This paper presents the development of a fully customized machine learning tool that has been designed as a decision support tool for a particular task for non-data scientist reservoir and production engineers. The architecture of this tool includes independent modules that can be re-trained in the future once additional production data becomes available from the fields. We attribute the success of this research to the fact that there was active collaboration among domain experts. Critical physical properties were included in this study, including chemistry studies of chemicals and limited field trials in validating the developed model. The resulting machine learning model can be used by field engineers for planning chemical well treatments given the continuously updated past performance data.
Economic Evaluation of Polymer Flood Field Test in Heavy Oil Reservoir on Alaska North Slope
Authors: Keith Cody , Wang Xindan , Zhang Yin , Dandekar Abhijit , Ning Samson
Published: September 2022

Since August 2018, a polymer flooding field pilot has been underway in an unconsolidated heavy oil reservoir on the Alaska North Slope (ANS). Previously, a reservoir simulation model was constructed and calibrated to predict the oil recovery of the field test; it demonstrated that polymer flooding is technically feasible to significantly improve oil recovery from heavy oil reservoirs on the Alaska North Slope. However, the economic performance of the pilot, critical to determining its success, has not been investigated, which is another key metric used in assessing the overall performance of the field pilot. Therefore, this study focuses on evaluating the project's economic performance by integrating the calibrated simulation model with an economic model. The investigation results demonstrate that the project value remains profitable for all polymer flood scenarios at conservative economic parameters. Thus, the use of polymer flooding over waterflooding is attractive. However, the predicted value changes meaningfully between the scenarios, emphasizing that a simulation model should be taken as a "living forecast". Subsequently, an economic sensitivity analysis is conducted to provide recommendations for continued operation of the ongoing field pilot and future polymer flood designs. The results indicate that a higher polymer concentration can be injected due to the development of fractures in the pilot reservoir. The throughput rate should remain high without exceeding operating constraints. A calculated point-forward polymer utilization parameter indicates a decreasing efficiency of the polymer flood at later times in the pattern life. Future projects will benefit from starting polymer injection earlier in the pattern life. A pattern with tighter horizontal well spacing will observe a greater incremental benefit from polymer flooding. This case study provides important insight for the broader discussion of polymer flood design from the economic perspective. It illustrates how expectations for performance may change as additional data is collected. It also formalizes the concept of "point-forward utilization" to evaluate the incremental efficiency of additional chemical injection.
Using Predictive Software to Minimize Risks of Lost Circulation, Multi-Stage and Liner Cementing Jobs
Authors: Al Ghafri Alamjad , Turner Jamie
Published: September 2022

Cementing hydraulic modeling software is a powerful tool used to evaluate the hydrostatic impact during cementing placement. The input of accurate data can simulate the Equivalent Circulating Density (ECD) impact of the cementing design in the wellbore. The same software can utilize Computational Fluid Dynamics (CFD) to create a 3D model simulating the effects of fluid intermixing and flow, plus the final position of total fluids to understand and minimize risks presented in multi-stage and liner cementing jobs. ECD is traditional primary area of focus measuring the impact of friction pressure from fluid flow and the total hydrostatic pressure from fluid density at a depth. The software shows downhole annular pressures and ECDs versus fluid volume pumped into the wellbore at multiple depths. The CFD modeling capability also empowers the user to understand the impact of intermixed fluids at their final depth of placement. The software user is better equipped to incorporate positive design changes to influence the outcome of the real time operation. Whether ECD management or displacement efficiency improvement, the user can make knowledgeable decisions based on iterative simulation cases by assessing and monitoring specific variables within the software.
What to do with Produced Solids After Separation: Dewatering, Transport, and Disposal
Authors: Rawlins Charles H.
Published: September 2022

The primary role of offshore solids handling is to properly remove and dispose of produced solids, without interruption or shutdown. Most sand management papers focus on the separation component only (i.e., wellhead desander, jetting system, or produced water desander). However, 80% of the capital expenditure (CAPEX) is attributed to separating devices, and 80% of the operating expenditure (OPEX) in sand handling operations involves dewatering, transport, and disposal (D-T-D). The present work outlines questions to ask during facility design and provides guidelines, calculations, and examples on each of the sand handling steps to be implemented after separation. Sand handling is not a waste-stream treatment exercise but a critical flow assurance issue. Following all five steps of Facilities Sand Management (FSM) will maximize hydrocarbon production and minimize the operating costs.
Correlated Inversion of Complex Dielectric Dispersion and NMR Measurements in Conventional Carbonates
Authors: Funk James , Myers Michael , Hathon Lori
Published: September 2022

Combinations of dielectric and NMR measurements are frequently used to improve saturation modeling in complex situations, often incorporating the concept of wettability. Due to the two methods' distinct tools and physical mechanisms, the interplay of the electrical and magnetic fields and their constitutive equations are generally not addressed. This is directly counter to the situation with the medical imaging modalities, magnetic resonance electrical properties tomography (MREPT) and magnetic resonance electrical impedance tomography (MREIT), where field-specific polarizations and relaxations are used to enhance the contrast.

Both electrical and magnetic (EM) fields at the frequencies typically encountered in laboratory and logging environments impart molecular motions impacted by pore structure. In both instances, restricted motions are reflected in their individual responses' time or frequency domain. Using time-domain relaxations and variations in both EM fields, this work focuses on the practicality of using NMR and dielectric relaxation comparisons originally proposed by Bloembergen, Purcell, and Pound (BPP). Similar to the dipolar relaxation equivalence in the BPP model, we develop a relaxation time correlation assuming representative Maxwell-Wagner relaxations for the key pore components demonstrated by Myers.

The distributions of dielectric relaxation times evident in carbonate dispersion curves from 1 – 300 MHz were quantified using the Havriliak-Negami (HN) model. The quantifications are then used to evaluate characteristic dielectric dispersions curves generated from a dielectric model introducing multiple pore systems in carbonates. The modeled distributions are spectrally mapped to the NMR T2 distributions based on Debye shielding distances correlated with the conductivity. The interplay of pore connectivity and surface and bulk diffusivity are modeled using a "two-fraction fast exchange model" by Brownstein and Tarr.

Using dielectric and NMR experiments along with a combination of micro-CT and SEM imaging techniques, the NMR-based spectral distribution of dielectric relaxation times demonstrates that variable-length scales and fractal dimensions accessed through the dielectric dispersion measurements are more extensive than that implied by the standard reference to the "texture" of a carbonate sample. We also show that the modeled distributions are closely correlated with the conductivity and provide improved petrophysical insight for the frequently used Archie exponent combination (MN) associated with the water tortuosity.
Lowering Carbon Foot-Print by Increasing Operational Efficiency Using Adaptive Machine Learning
Authors: Singh Ajay , Miller Stephen , Brinkley Michael
Published: September 2022

It is essential to meet climate goals outlined in the Paris Agreement that the Oil and Gas industry reduces carbon emissions along with achieving production targets. The described body of work herein provided a machine learning (ML) framework to predict upcoming shutdown events which often resulted in event driven flaring with increased carbon emission. Such flaring events are common and many times are not analyzed for root cause until unless they start to become bigger problems or occurred numerous times. One of the primary reasons for event driven flaring is poor process and equipment reliability. Therefore, ML framework is focused towards developing a tool to improve system reliability. Some of the challenges predicting these events are changing process, equipment and operating conditions and highly interactive processes which often results in generating false alarms. To address this issue, this paper proposes an adaptive ML framework where principle component analysis (PCA) is used for anomaly detection. PCA model is updated every hour so that it can update the model with changing operating conditions. PCA model utilizes sensor data from upstream and downstream of the process under consideration along with available equipment data to predict shutdown event.

In the current work, three level of alarms are generated using threshold levels corresponding to 95%, 99% and 99.9% of confidence limits for D-statistics on the training data. These alarms correspond to 1) low risk (only reports and no urgency to act), 2) moderate risk (event review is must) and 3) high risk (urgency to act) respectively. The ML model was also able to identify top N sensors which contribute towards shutdown event. The solution was applied to an offshore compressor train and it was found that without using adaptive framework, only 70% of shutdown events can be predicted with lot of false alarms. Using adaptive framework all of the shutdown events were predicted. Although, false alarms were predicted, however the frequency at which false alarms were generated was found low and acceptable for practical application. Two case studies were presented which revealed that adaptive ML framework was found effective to capture events several hours in advance. Root-cause analysis was automated using contribution charts and top 4 key sensors were identified which were contributing towards various shutdown events and were resulting in 1 to 10 hours of flaring. After root-cause analysis for each event, a mitigation solution was presented to avoid similar repeated shutdowns. Several process and operational setting changes were identified to avoid future shutdowns caused by compressor train. Overall, annually 46MMSCF reduction in flare volume and 30-thousand-barrel reduction in lost oil production volume was identified.
A New Viscosity Sensing Platform for the Assessment of Polymer Degradation in EOR Polymer Fluids
Authors: Gonzalez Miguel , Thiel Tim , St. Michel Nathan , Harrist Jonathan , Buzi Erjola , Seren Huseyin , Ayirala Subhash , Maskeen Lyla , Sofi Abdulkarim
Published: September 2022

Polymer degradation during Enhanced Oil Recovery (EOR) can have large impact on recovery rates during polymer flooding. In the field, few practical solutions exist to perform quality control/assurance (QA/QC) on EOR polymer fluids at surface and no solutions exist for measurements downhole. Here, we present the development of a miniaturized sensor that can be used to detect the onset of polymer degradation by measuring the viscous properties of EOR polymer fluids. The device was tested on samples collected from a polymer flooding operation. We describe its integration into wellsite portable systems and into an untethered logging tool for cost-effective routine measurements downhole. The sensors are based on millimeter-sized piezoelectric tuning fork resonators. The viscosity and density of the fluids was measured from the energy dissipation and the resonant frequency obtained from their vibrational spectra. The devices were specially designed for use in high-salinity polymer fluids. They were tested and validated on samples collected from a single well polymer flood trial. A miniaturized electrical measurement platform was then designed for use at surface in the field and for use in a compact untethered logging tool for quick and inexpensive deployment downhole. The devices were initially calibrated in the laboratory and then tested on samples collected from the field. These two field-collected solutions were used to preflush the formation before injecting surfactant-polymer solution and as a polymer taper to drive the injected surfactant-polymer solution, respectively. The obtained viscosity values correlated very well with those obtained from standard laboratory measurements. Therefore, the changes in viscosity due to reduction in the molecular weight of the polymer, as measured with the miniature devices, can be used to assess whether degradation has taken place. A miniaturized electrical measurement platform was then tested in comparable polymer fluids for use in the field and obtained comparable results.

The platforms described here provide a simple, cost-effective, and user-friendly platform for the detection of polymer degradation in the field, thus providing valuable information in real-time during costly polymer flooding operations.
Nonequilibrium Phase Behavior Plays a Role in Solvent-Aided Processes
Authors: Bayestehparvin Bita , Farouq Ali S. M.
Published: September 2022

Several studies focused on experimental results of nonequilibrium phase behavior and its numerical implementation for solvent-aided recovery processes. However, there is a clear discrepancy between optimistic simulation results and the less than-favorable performance of pilot projects. The intent of the present study is to demonstrate the importance of nonequilibrium phenomena in terms of oil production, steam-oil ratio (SOR), and solvent-oil ratio. This evaluation is based on solvent type, process type, operational conditions, and reservoir properties.

A pore-scale simulator was developed and used to investigate solvent propagation in porous media based on solvent properties (density, viscosity, and diffusion coefficient). Several models were used to investigate the effect of petrophysical properties, e.g. tortuosity, on the upscaled diffusion coefficient, dispersion tensor, and interfacial mass exchange coefficient. In the next step, an in-house thermal reservoir simulator, incorporating nonequilibrium phase behavior, was used to investigate the effect of the particular recovery process.

The butane expanding-solvent steam-assisted gravity drainage (ES-SAGD) process with nonequilibrium assumption leads to a 50% reduction in incremental bitumen production in early stages compared with the result for equilibrium assumption. This number reduces to 20% when the chamber becomes mature. It is shown that lighter and more volatile solvents, such as propane show significant deviation from equilibrium behavior compared to heavier and less volatile solvents. The results for ES-SAGD show that close to the chamber edge or in the cold part of the reservoir, the ratio of nonequilibrium constants to standard K-values is very large which means that solvent/oil system is not at equilibrium. Inside the chamber, the temperature is higher which increases the diffusion coefficient of the solvent. In addition, the contact time for oil and solvent for gridblocks inside the chambers is high enough, so that the nonequilibrium constants are close to the K-values. It was found that nonequilibrium behavior can be diminished by increasing solvent-oil contact time in huff and puff processes.

This study presents a systematic approach to evaluate a given solvent-aided recovery process from the pore to the reservoir scale and provides the guidelines to assess the importance of nonequilibrium phase behavior in both experiments and numerical studies. Based on the findings of this work, the performance of solvent-aided recovery processes can be predicted more realistically, and the process can be optimized for given operating and reservoir conditions and solvent type.