bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.