bluuebunny/crossref_metadata_2025 huggingface dataset:
Understanding ESP Performance Under High Viscous Applications and Emulsion Production
Authors: Pastre Luiz , Biazussi Jorge , Monte Verde William , Marins Jean , Melchuna Aline , Cowie Mark , Vergara Luis , Navarro Ivamberg , Lemos Daniel , Alfonzo Antonio , Ramos Leudy , Bannwart Antonio
Published: April 2022

Although being widely used as an artificial lift method for heavy oil field developments, Electrical Submersible Pump (ESP) system performance in high viscous applications is not fully understood. A miscomprehension of challenges and equipment performance in such conditions might lead to operation inefficiencies and equipment failures. This paper presents results of single-phase and multiphase tests performed by University of Campinas (UNICAMP). It also presents operation data, lessons learnt, and failure examples gathered over 10 years of ESP operation in Peregrino field which is a heavy oil, high viscous oilfield offshore Brazil operated by Equinor.

Affinity laws commonly used for ESP simulations don't hold true for high viscosity applications. Hydraulic performance of centrifugal pumps is affected by fluid parameters like viscosity and density; operation parameters such as flow rate and rotational speed; and specific stage design characteristics. To determine degradation in head and efficiency as well as power requirement increase in viscous applications, Equinor performs one-phase high viscosity flow loop test to qualify each stage type prior to deployment in Peregrino field.

For the qualification of ESPs, single phase qualification tests are performed using mineral oil with viscosities specifically chosen to cover the viscosity range of the specific field. Each stage type is qualified using a prototype with reduced number of stages due to flow loop limitations. Qualification tests for the Peregrino field confirmed that affinity laws are not accurate for high viscous applications and provided important insights regarding pump performance that are used in equipment specification and system surveillance.

The UNICAMP research team has designed and performed multiphase flow tests to evaluate emulsion formation inside centrifugal pump stages and effective viscosity behavior. Phase inversion phenomenon investigation was also included in studies. Studies performed using a prototype stage allowed visualization and evaluation of oil drops dynamics inside the impeller in different rotational speeds. Two phase flow loop tests investigated the shear forces influence in effective viscosity inside pump stages and downstream pump discharge. Phase inversion phenomenon was also a point of great interest during the studies. Data gathered during lab tests was used to evaluate accuracy of mathematical models existing in the literature when a centrifugal pump is added to the system. Hysteresis effect associated to catastrophic phase inversion (CPI) was confirmed and replicated during flow loop tests. Such behavior can be related with operation parameters instabilities and equipment failures noticed in actual application in Peregrino field which are also presented in this paper.
Transforming Asset Maintenance and Optimisation Through Autonomous Operations
Authors: Pierson Nicholas , Clarke Richard , Mikhailov Oleg
Published: April 2022

Autonomous Operations is about utilising the latest available fit-for-purpose technology in operations, maintenance, inspection, and asset optimisation. It involves collecting operating data such as visual, thermal, low- and high-frequency acoustic, FLIR (Forward Looking InfraRed), and gas-sensing through devices mounted on autonomous robots or drones, possibly mixed with stand-alone fixed devices.

This "mobile sensor" approach is ideal for existing infrastructure where adding enhanced sensing capabilities with an IoT solution is cost prohibitive. It is flexible: if a site is modified, the robots can be given a new route to monitor. It is also future proof as sensors can be easily upgraded as technology advances.

Autonomous robot inspection is enabled by a software platform designed for full processing "at Edge" as a typical robotics inspection generates tens of gigabytes of data per hour. Data pre-processing, construction of detailed 3D temperature, vibration amplitude and gas concentration maps, identification of anomalies, their classification by criticality and communication to operators is done by computers at the facility.

Time lapse analysis is used to process the data either in-situ, or remotely, depending on site connectivity. Reports are automatically generated indicating whether the site is functioning within normal working conditions, or an anomaly has been detected. The electronic reports contain the 3D model and are viewable on mobile devices providing the field engineer with detailed diagnostic data literally at their fingertips.

The process builds and maintains the living digital twin without having to expend engineering hours to update 3D models. This process not only removes the need for people, which reduces cost and safety risk, but also improves reliability by increasing the available data set and reducing inspection intervals. By utilising data analytics and artificial intelligence, the anomalies detected can be risk ranked, prioritised and scheduled based on a latest acceptable completion date.

This technology has been tested through a pilot program at an activated carbon plant in Wyoming during the summer of 2021. In this paper, we will present a case study of this pilot, covering installation, data acquisition and processing, as well as the key challenges and results.
Development of Tethered Flying Robots with PAUT Capabilities for C-Scan and In-Service Inspection Mini-Crawler/Uav: Marsupial Robotic Approach
Authors: Tavakoli Reza , Kamalinejad Pouya
Published: April 2022

Avestec has successfully developed proprietary contact-based flying robots (SKYRON™) for ultrasonic (UT) A scan measurement of metallic surfaces. The technology has been successfully validated with many asset owners in onshore and offshore industrial campaigns.

Although the flying contact-based robot can easily access most asset surfaces to perform inspections, it has some limitations when it comes to challenging and dense spaces. Such shortcoming is exasperated specially for pipe inspection in offshore environments. Pipes are usually located in the vicinity of one another or in close proximity to walls or other structures, which makes it practically impossible for an unmanned aerial vehicle (UAV) to reach to every side of the pipe and carry out circumferential (3/6/9/12) measurement as needed.

In order to enhance the advantages of contact-based aerial robots and overcome the aforementioned limitations, a marsupial robotic methodology has been followed by means of combining the proprietary contact-based UAV and a custom omni-directional mini crawler in a collaborative robotics fashion. An advanced marsupial connection between the two robots both in terms of mechanical (e.g., carrier/passenger roles) and control mechanism (e.g. master/slave roles) and versatility of the task deliveries can potentially introduce a lot of advantages over a single flying robot for asset inspection.

In addition, Phased Array Ultrasonic (PAUT) sensory package has been integrated into the marsupial robot to expand NDT capabilities for inspection including thickness measurements, corrosion mapping, defect detection (e.g. such as cracks, voids, and pits caused by corrosion), weld inspection and many more.
Correlation Pattern Exploration of Wave Energy Density Hotspot Distribution and Hurricane-Induced Wave in Gulf of Mexico
Authors: Gu Chengcheng , Li Hua
Published: April 2022

The activities in the Gulf of Mexico are thriving with a considerable number of oil and gas platforms operating in this area. Wave power can provide a substantial portion of clean power to substitute for the inefficient gas turbines used on the platforms. Wave power density (wave energy flux) is one of the ways for directly assessing the potential and available wave power. The task of a wave energy capture device is to effectively capture the wave energy flux. The average wave power density is generally between 20 kW/m and 65 kW/m in the Gulf of Mexico. During tropical storms or hurricanes, the theoretical wave power density could reach 1,600 kW/m. However, higher wave height and period could damage the energy capture devices. This project explores the correlation pattern between available wave power density hotspots and hurricane-induced wave distribution within 10 years (2010-2019) using a deep convolutional neural network.

The correlation pattern was explored and validated by applying meteorological data in the Gulf of Mexico area from the NOAA WAVEWATCH III system. A wave power hotspot was defined as an event when wave power density over a certain threshold during a period of time in a predefined location. The distribution of wave power hotspots has the segmentation threshold on wave power depending on the wave energy converter, location, wave height and wave period. The meteorological data from WAVEWATCH III was visualized as images of wave power density in 3-hour time steps, which were used for wave power density hotspot identification, classification and localization through a deep convolutional neural network.

With the distribution of wave power density hotspots along with the coordinates attached through matching the local wave and hurricane-induced wave conditions and the GIS mapping support, the project was able to reveal the impacts of hurricanes based on pattern recognition between the wave power hotspots distribution and hurricane-induced wave track distribution. Meanwhile, the comparison of the wave power density hotspot distribution with extreme weather conditions and under the regular circumstances was analyzed. The results could also provide feedback to and support wave energy harvesting or hurricane forecasting and harvesting.
Improving Erosion Wear Life of Completion Equipment in High Flow Rate Conditions by Numerical Design Optimization for Influx Equalization
Authors: Chochua Gocha G , Kumar Amrendra , Gurses Sule F. , Rudic Aleksandar , Dikshit Ashutosh , Sinha Nityanand
Published: April 2022

The durability of sand screen completions is essential to longer well life, especially for high rate wells with sand screen erosion concerns. An excessive fluid flow enters the conventional screens near the heel or high permeability/fracture zones, causing premature sand control loss. The high rate screens with a simulation-driven approach address this concern by achieving the annulus-to-tubing flow equalization and reducing the influx spike near the heel or high permeability/fracture zone.

The study presents a comprehensive modeling approach including a single-well model workflow for initial production screening along the wellbore with different reservoir conditions, which provides input to the novel multiscale 3D-2D-3D computational fluid dynamics (CFD) modeling technique to design or validate high-rate completions for the specific operating conditions. The principle of operation is based on equalizing the production influx along the screen by achieving the distributed inflow control devices (ICD) effect on the basepipe. The modeling approach was used to compute maximum local velocities in the vicinity of the screen near the heel under 39,000 RB/D of ultra-light oil production in one case and 200 MMscf/D of gas production in another.

The design methodology is validated through erosion and sand retention tests performed to verify the screens’ correct slot/gauge size. The high-rate completion case history consists of seven deepwater wells with chemical tracers. The novel design and the modeling methodology are validated by physical erosion tests and verified through field installations.
Getting Serious with Net-Zero: Implementing Large-Scale Carbon Capture and Storage Projects in ASEAN
Authors: Lau Hon Chung , Zhang Kai , Bokka Harsha Kumar , Ramakrishna Seeram
Published: April 2022

As a region, the ten nations of ASEAN emitted 1.65 Gtpa CO2 in 2020 from the combustion of fossil fuels. Analyses reveal that ASEAN's renewable energy resources are low to moderate and unevenly distributed geographically. Furthermore, there are substantial sustainable issues related to hydropower and bioenergy. The current rate of addition of new renewable power capacity is too slow to allow ASEAN countries to achieve net-zero by the middle of the century. More tools, in addition to renewable energies, will be needed. It is found, however, that carbon capture and storage (CCS) is a key enabling technology to decarbonize ASEAN's fossil-fuel power and industrial plants and to produce blue hydrogen needed to decarbonize the industry sector. Furthermore, results of CO2 source-sink mapping exercises show that there is enough capacity in major sedimentary basins in ASEAN to permanently store two centuries of anthropogenic CO2 emission. Six first mover large-scale CCS projects in ASEAN with potential to mitigate up to 300 Mtpa CO2 from Singapore, Indonesia, Malaysia and Thailand have been identified. Furthermore, the steps needed to implement these CCS projects are also discussed.
Design of a 750 °F, 15 K packer for Enhanced Geothermal Systems, Supercritical CO2 - Sequestration and SAGD: Energy Transition Through Technology Synthesis
Authors: Roy Ting , Ben Naceur Kamel , Singh Manjinder , Markel Daniel , Harp Leonard , Ardic Hifzi , Wilkinson Christian , Roy Indranil
Published: April 2022

Several technology building blocks are required for the growth of geothermal energy to match what is needed to enable the energy transition. One key technology required in this field is a reliable packer that can withstand the extreme environments frequently seen in geothermal wells: elevated temperatures (750°F/400°C); high differential pressures (up to 15K psi); and extremely corrosive fluids.

The authors present a concept which is thought to offer a promising solution to this challenge. It builds upon the wealth of experience obtained by operators designing packers for the oil and gas wells and couples this with advancements in materials processing technology in elastomers, composites, and metals.

The presented concept uses the sealing advantages that elastomers are known to offer, with a compound specifically designed by a strategic partner. This elastomer is then surrounded by a layer of flexible gel-insulation of extremely low thermal conductivity to shield it from the intense heat seen in geothermal wells. It is predicted that the elastomer core will see temperatures no higher than 400°F when the surrounding environment is at 750°F. The system is then encased in a Grain Boundary Engineered (GBE) nano-metallic flexible skin material to prevent contact with corrosive fluids. Though the elements are the greatest challenge, the other components of the packer design are also specially designed with material processing techniques tailored to both enhance mechanical properties and corrosion resistance.

The technology behind the design is detailed and has been proven. The integrated concepts will be further tested at subcomponent level to show their merit, before integration into a full system for qualification to 750°F, 15K psi.
Flow Pattern, Pressure Gradient Relationship of Gas Kick Under Dynamic Conditions
Authors: Obi Chinemerem Edmond , Falola Yusuf , Manikonda Kaushik , Hasan Abu Rashid , Rahman Mohammad Azizur
Published: April 2022

The warning signs of possible kick during drilling operation can either be primary (flow rate increase and pit gain) or secondary (drilling break, pump pressure decrease, and stroke increase). Likewise, the drillers rely on the pressure readings at the surface to have an insight into in-situ downhole conditions while drilling. The surface pressure reading is always available and accessible. However, understanding or interpretation of this data is often ambiguous. This study analyses significant kick symptoms in the wellbore annulus while drilling/circulating.

We have tied several observed annular flow patterns to the measured pressure, and flow data from the surface during water-air, and water-carbon dioxide complex flow. This is based on experiments using a 140 ft high tower lab, with a hydraulic diameter of about 3 in. The experiments have been carried out under dynamic conditions to simulate circulating drilling mud from the wellbore. We used both supervised and unsupervised learning techniques for flow regime identification and kick prognosis. These include an Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Trees, K-Means and Agglomerative Clustering. All the machine learning techniques used in this work made excellent predictions with accuracy greater than or equal to 90%. For the supervised learning, the decision tree gave the overall best results with an accuracy of 96% for air-influx cases and 98% for carbon dioxide influx cases. For the unsupervised learning, K-Means clustering was the best, with Silhouette scores ranging from about 0.7 to 0.8 for the rate data clusters, and 0.4 to 0.5 for pressure data clusters. The mass rate per hydraulic diameter and the mixture viscosity also resulted in the best type of clusters. This is because this approach accounts for the fluid properties, flow rate, and flow geometry.

The estimation of the influx size and type is highly dependent on the duration of kick and the overbalance kick influx pressure. The quantity of the mass influx significantly controls the flow pattern, pressure losses, and pressure gradient as the kick migrates to the surface. The resulting turbulent flow after the initial kick (After Taylor bubble flow) varied with duration of kick, average liquid flow rate, influx type, and drilling scenario. Surface pressure readings can be tied to flow regime to better visualize well control approach while drilling.

This works provides an alternative and easily accessible primary kick detection tool for drillers based on measured pressure responses at the surface. It also relates this pressure data to certain annular flow regime patterns to better tell the downhole story while drilling.
Completing the Production Logging Puzzle Through Data-Driven Integrative Analysis
Authors: Bari Abdul , Khan Mohammad Rasheed , Zubair Talha , Atiq Mustafa , Shahid Muhammad Hasan , Awase Azar , Tanveer Muhammad Sohaib , Sarili Mahmut , Panhwar Aadil , Ahmed Qazi Ismail , Arsalan Ullah Hafiz Muhammad
Published: April 2022

In response to the highly volatile industry situation post-Covid19, E&P companies are abandoning or delaying exploration projects. It is anticipated that to meet growing energy demand in the future, it would require robust reservoir surveillance to improve individual well productivity. Production logging plays a key role in developing reservoir flow characterization and its integration with multiple data sources can allow for a holistic well dynamic description, leading to production enhancement on a well-by-well basis. Multiple case studies are presented in this work that propose a data-driven, integrated workflow which combines production logging data with plethora of under-utilized data sources to constitute an investigative analysis to solve water production conundrum.

The workflow developed as part of this work incorporates three distinct stages. Firstly, an in-depth data analysis is conducted utilizing all historical production data/trends and well events to link it with water production as each will have its distinct impact on the type of production logging tools/techniques that would be deployed on the subject well to achieve job objectives. Secondly, real-time production logging is conducted to ensure job objectives as defined in stage one is being met which is crucial to the resultant wellbore zonal profiling. Finally, once the production log visual representation is created, various other open-hole petrophysical and cased-hole well integrity logs are analyzed to make sense of the derived results and suggest reasoning to the state of the reservoir production pertaining to producing water. The case studies discussed in this paper include the use of petrophysical logs, MDT formation testing data, ultrasonic cement evaluation logs, and production log time-lapse analysis.

Integrating various data sources with production log analysis allowed for establishment of water production sources in the various case studies discussed. In one case, production related ambiguity was resolved by integrating formation testing and production logging data together. Another analysis revealed channeling behind the casing through a micro-channel in the cement which caused water to encroach shallow perforations. This highlighted the importance of azimuthal cement logging to detect small channels even when omni-directional sonic measurements are depicting good cement placement. Finally, through integrated time-lapse production log analysis, it was discovered how wellbore dynamics were changing chronologically.
Hybrid Modeling for Multiphase Flow Simulations
Authors: Henriksson Johan , Wollebæk Lars , Yang Zongchang
Published: April 2022

This paper provides an overview of the following three modeling approaches – Physics-based Modeling, Machine Learning, and Hybrid Modeling (Physics-based Modeling & Machine Learning combined), and addresses their applicability for multiphase flow simulations in specific use-cases.

Physics-based modeling builds on well understood concepts in, e.g., thermodynamics, fluid dynamics, fluid modeling and optimization techniques. It requires deep domain knowledge as well as accurate fluid data and may incur significant computational cost.

Machine Learning systems are based on learning algorithms, which find relationships between sensor data and output variables in a training dataset. The approach requires a good understanding of the learning algorithms and statistics. Small datasets or changes in operational conditions limit the suitability of this approach.

Hybrid Models combine Physics with Machine Learning, and these models are on a sliding scale between pure Physics-based Models and pure Machine Learning Models. The individual use-case defines how the Hybrid Model is configured and where it sits on the sliding scale. Here, we will investigate three different use-cases:

Physics supporting Machine Learning: Use physics to generate data to support training of machine learning models when data is sparse. Machine Learning supporting Physics: Use machine learning to provide additional input to physics modeling when available data does not suffice to achieve accurate numerical solutions. True Hybrid: Close the circle and use physics for feature engineering to improve the machine learning models, which then provide synthetic data as input to physics-based simulations.

We observe that there is not one universal solution. On the one hand, flow simulations may address situations which are stochastic in nature and not deterministic. Here, a machine learning model based on physics-based simulations fits the purpose well. On the other hand, there are situations when machine learning can deduce relations between parameters such that you can provide additional input into physics-based models. One such example is to address the oil-water split at very high water cuts. By adopting the appropriate combination, the hybrid approach results in superior accuracy and offers the ability to address a broader range of applications than physics or machine learning alone.
Acoustic Induced Vibration AIV Resistance of Pipework: Fatigue Based Approach and Beyond Screening Methods
Authors: Katam Reuben , Knox Jeff , Blair Tamara
Published: April 2022

Acoustic Induced Vibration, more commonly called AIV, is the broadband excitation of pipework when high-pressure gas is let down through pressure-reducing devices. The most widely adopted screening method for AIV assessment is the Energy Institute [EI] guidelines; however, the guidelines primarily employ a risk-based assessment method and lacks an approach to measuring the resistance level of the pipework against AIV. A fatigue-based approach to measuring the resistance of pipework against AIV is presented in this paper.

Previous studies have shown that correlations exist between acoustic modes in the pipe and structural mode frequencies. This paper takes this notion further, translating the structural modes into a predictable fatigue life using S-N curves. A typical AIV broadband excitation level can range anywhere between 100Hz – 3000Hz. Modal analysis of the pipework near discontinuities is performed, and frequencies are screened at levels nearly up to 2700Hz. The maximum modal stresses are taken at the elements for each mode, and the total stress cycles are counted to estimate the fatigue damage. A damage sum of unity is then used as the basis for calculating the acoustic fatigue life.

In addition to acoustic cycles, AIV-affected lines on the offshore floating facilities such as FPSO's and FLNG's are also subjected to functional (pressure and temperature) and environmental (vessel motion and acceleration) cyclical loads. This often leads to insufficient fatigue damage allocation resulting in low acoustic fatigue life. Most of the AIV-related failures can be attributed to high cycle fatigue failures at the geometrical discontinuities in the pipework. Small-bore discontinuities on the main header tend to display lower acoustic fatigue resistance than large diameter connections. Early identification and mitigation of sensitive discontinuities in pipework under cyclical loads and maximizing the damage allocation can improve acoustic fatigue life.

Concerns surrounding the risk of AIV failures on offshore facilities have become more prevalent in recent times due to higher plant output and increased gas flow rates and velocities in the pipework. When the screening method predicts concern for AIV, a fatigue-based approach can be used to establish acceptance criteria and help prevent expensive redesign of the piping system during execution.
Shallow Gas and Hydrate Hazard Prediction in the Gulf of Mexico Using a Probabilistic Machine Learning-Reservoir Simulation Workflow
Authors: Daigle Hugh , Jacoby Gabrielle , Carty Olin R.
Published: April 2022

In shallow marine sediments, gas accumulations and hydrates are significant geohazards to subsea infrastructure, drilling, and production. Therefore, predicting their occurrence is crucial to ensure offshore drilling safety and submarine infrastructure security. In this study, we generate predictions with uncertainty estimates with the goal of providing ageohazard assessment before any shallow hazard surveys are performed.We used a geospatial machine learning model to predict total organic carbon (TOC) and porosity at the seafloor in the northern Gulf of Mexico, and model sedimentation and consolidation in one dimension (1D) with microbial methanogenesis. Our model assumed that seafloor organic carbon is the source material for shallow hydrocarbon occurrences. The machine learning model outputs and uncertainties were sampled statistically to generate a suite of seafloor property realizations that were fed into our 1D model. Our predictions illustrate that gas and hydrate are more likely to be present along the shelf where seafloor TOC values are high, and are less likely to be present in deepwater areas (>500 m water depth) where seafloor TOC values are low. The results show that shallow water depth, lower sedimentation rate, and higher seafloor TOC are correlated with higher predicted gas saturations and shallower gas accumulation.Deepwater areas with significant reported oil production, such as AlaminosCanyon Block 857 (Great White), Green Canyon Block 640 (Tahiti/Caesar/Tonga), and Garden Banks Block 215 (Baldpate/Conger), are less likely to have shallow gas hazards. Any seafloor seeps identified in areas of high drilling activity likely originate from deep reservoirs, not from shallow gas accumulation.This work provides granular predictions of shallow geohazards on a basin scale and offers a holistic approach to identifying shallow hazards using big data and machine learning techniques. Leveraging geospatial machine learning models improves the predictions of subsurface hydrocarbons, despite sparse sampling of seafloor properties, and can be made pre-drill and without additional observations like sediment samples. This method can complement and augment existing hazard survey techniques.
Rock Berms as Axial Restraint for Flowlines on Soft Soil: Improving Design Through Centrifuge Modelling
Authors: O'Beirne Colm , Bransby Fraser , O'Loughlin Conleth , Watson Phil , Banimahd Meysam , Kuo Matthew , Vojkovic Mason , Zhang Rose
Published: April 2022

Rock berms are used to restrain flowlines from moving axially by adding vertical load to enhance resistance. Should the contact pressure be reduced at the flowline-berm interface, such as in response to ‘arching’ of the rock berm due to pipe settlement, this resistance may be substantially reduced. This study shows how the development of axial restraint forces is complex, with shear (‘friction’) at the pipe-berm and pipe-soil interfaces influenced by system settlement.

We examine the restraint provided by rock berms through three phases of centrifuge model testing. Phase 1 isolates the pipe-berm interaction through ‘trapdoor’ tests on a false (rigid) seabed using actual berm profiles and scaled rock. Phase 2 models a slightly overconsolidated clay seabed with load from simulated berms held constant, thereby isolating the effects of pipe settlement and cyclic hardening at the pipe-soil interface. Finally, Phase 3 - which most closely represents the actual field behaviour - models the berm/pipeline/seabed system to investigate the combined effect of pipe settlement, arching and frictional response.

The trapdoor tests identify that berm arching can occur in situations where simulated settlement of the flowline leads to redistribution of the rock berm weight, such that the berm weight acting on the flowline falls towards zero. Axial cycling is shown to help recover the berm's effectiveness, but is dependent on the amount of settlement - with greater settlements generating less axial restraint recovery. The constant vertical load tests confirm that axial resistance developed along the pipe-soil interface is strongly influenced by the vertical load delivered by the rock berm. Testing showed that axial resistances increase with consolidation and hardening, and also that settlements were modest and observed to be a function of vertical load. Axial resistances developed in the final phase of testing were the highest of all, as the tests include all restraining actions. These tests suggest that arching does occur under ‘realistic’ conditions - but that the effect is modest and largely eliminated by ongoing cycling.

The findings reduce current design uncertainties and have already been incorporated in an offshore project where rock berms are being used to mitigate axial movements of a flowline. In particular, this novel centrifuge modelling confirms the potential for berm arching and loss of restraint, but also shows that arch collapse leads to recovery of flowline restraint - enabling the potential to reduce rock berm volume compared to the case of assuming arching leads to permanent loss of resistance.
Integrated Approach to Understand Variable Water Gas Ratio Production in Naturally Fractured Carbonate Reservoir
Authors: Aslam Muhammad Adeel , Siddiqui Faisal Anwar , Bari Abdul , Khan Imran , Siddiqui Dr. Fareed Iqbal , Khan Mohammad Rasheed , Abbasi M Yasir , Tanveer Muhammad Sohaib
Published: April 2022

Innovation is pivotal for sustainability in today's dynamically changing E&P industry. Global industry downturn and the pandemic has pushed operators towards squeezing expenditures and look for cheaper, but efficient alternatives to maximize production from producing/discovered resources. Carbonate reservoirs provide a unique set of formation and productivity evaluation related challenges due to the highly variable characteristics of the natural fractures. This work elucidates how production logging data, combined with multiple wireline logs, resulted in resolving water production ambiguity in a well drilled into a Naturally Fractured Reservoir (NFR). Value of integrative data analysis to extend production logging diagnostic capability will be the focus of this paper.

The well under consideration in this paper is a wildcat well located in a region which involves complex geology and intricate fracture systems. Accordingly, a two-tier methodical approach to characterize formation evaluation and assess well producibility was proposed and implemented through an integrated lens. Firstly, a combination of petrophysical and fracture image logs helped in comprehensive open-hole characterization. Next, to evaluate reservoir deliverability from the fractures, formation testing with 3D Radial probe and then extended well testing operation was performed, which confirmed hydrocarbon presence however, variable Water Gas Ratio (WGR) posed as a challenge, and it became critical to confirm the source of water for deciding future completion strategy. Finally, to solve the produced water conundrum, real-time production logging with integration of all open-hole data and mud log was performed to provide a definitive answer product.

Integration of borehole image logs allowed to delineate complex producing zones based on fracture clusters with classification of variable fracture types and linkage with production log zonal contribution profile. Moreover, analyzing mud logs in the same canvas as the production log, led to establishment of linkage between surface water production and drilling fluid losses at specific depths. Ultimately, produced water anomaly at different choke sizes was resolved through real-time production logging optimization which indicated water sump build up downhole due to lack of lifting capacity from nearby perforations at higher choke sizes. Integrating all relevant well data in a single layout including production logging results helped in achieving objectives and understanding well dynamics in context of fracture flow characterization, water production, and diagnostic capabilities.
Experimental Investigation of Wellbore Integrity of Depleted Oil and Gas Reservoirs for Underground Hydrogen Storage
Authors: Hussain Athar , Al-Hadrami Hamoud , Emadi Hossein , Altawati Faisal , Thiyagarajan Sugan Raj , Watson Marshall
Published: April 2022

The is wide consensus that combustion of fossil fuels and rising greenhouse gas emissions in the atmosphere are accelerating global warming. To avoid the dilemma of need for fossil fuels to provide energy supply and the need to reduce fossil fuel related emissions, it is critical to promote renewable energy as a viable option to satisfy the world's energy requirements. However, employing renewables to generate power necessitates the use of bulk storage to accommodate discrepancies related to where and when renewable energy is produced versus where and when it is needed. Underground hydrogen storage has the potential to support establishment of hydrogen as a reliable source of clean energy across the planet.

Where present, depleted oil and gas reservoirs, due to their existing infrastructure, can prove to be an attractive asset for underground hydrogen storage. One of the main challenges involved in the storage of hydrogen in the depleted oil and gas reservoirs is related to wellbore integrity. When hydrogen is injected or produced in the subsurface, it may get bin contact with cement around the wellbores. Hence it is necessary to investigate the effects of hydrogen interacting with the cement sheath. To study this in the laboratory, a core holder capable of simulating the wellbore conditions is used to conduct the tests. A 2" long, 1.5" diameter cement sample was placed inside the core holder and hydrogen was injected into it. Hydrogen was also injected into wet cement slurry to investigate possible stability of the cement samples.

The effects of injecting hydrogen on set cement are studied using a CT Scanner which demonstrates if there is any formation of cracks and micro-annuli in the cement. The cement sample is crushed afterwards, and the presence of hydrogen particles in the structure of cement is evaluated by X -Ray Diffraction. A neat 15.5 lbs./gal Class "H" cement which is common in the industry is used in this study.

Well integrity is a key success factor to establish the viability of underground hydrogen storage in the subsurface. For that we analyzed if the cement is good enough for hydrogen to be stored in depleted oil and gas reservoirs. We further studied the integrity of newly drilled wells when exposed to hydrogen.
WindFloat Performance Analysis for Smart Operation and Maintenance
Authors: Rinaldi Giovanni , Wagdy Ibrahim Moustafa , Germain Nicolas , Alexandre Armando , Martin Bruce , Rezende Flavia , Price Seth
Published: April 2022

In this paper, an overview of the performance analysis techniques and instrumentation adopted for the pioneer multi-MW WindFloat Atlantic project,the first exploiting commercial-scale semi-submersible floating wind devices, is presented. Initial results and how these will be exploited for the smart operation and maintenance planning of the floating foundations are discussed. The combined approach, novel for floating wind technology, is based on the use of modern component monitoring and control instrumentation, both installed on the floating platform and on the wind turbine, coupled to data analysis and visualization techniques. The data retrieved during operation on one of the three full-scale WindFloat devices deployed at sea are analyzed to confirm the validity of numerical expectations and derive insights for future development. The outcomes of the monitoring of planar and angular motions, especially in relation to design conditions, are presented. Post-processing and elaboration of field data is used to classify relevant information, which will be used for the refinement and calibration of the predictive algorithms that in turn will enable smart analytics and controller optimization. Although this is an on-going task, these methodologies will be combined to build a tailored Digital Twin of the device. In this way, a multi-disciplinary approach leading towardspossible extension of the project lifetime, minimization of operational expenses, and an increase in energy-based availability and electricity production, will be obtained. As such, this manuscript paves the way for the digitalization of the floating wind sector, with subsequent de-risking of this novel technology and related offshore interventions.
Cement Sheath Fatigue Failure Prediction by ANN-Based Model
Authors: Zheng Danzhu , Ozbayoglu Evren , Miska Stefan Z. , Liu Yaxin , Li Yijie
Published: April 2022

Zonal isolation is vital for safety operation of the well. Loss of zonal isolation can lead to sustained annulus pressure (SAP), which requires stopping production or even abandon of the well. The worst result of losing zonal isolation is the contamination of fresh water-table. This is irreversible and detrimental to all living creatures. However, cement sheath is exposed to pressure and temperature changes from time to time during the whole life of well. The temperature cycles can be raised up from completion process as well as the production process. The pressure cycles are caused by different operations, such as hydraulic fracturing. The objective of this study is to diagnose the fatigue behavior that caused by cyclic changing of pressure and temperature during different well operations. The main scope of the study is based on the fatigue failure cases in previous literature laboratory data. Artificial neural network (ANN) is used to predict the fatigue failure of the cement sheath.

The dataset is retrieved from 6 papers of One-Petro, which includes 314 cement fatigue failure cases. Inputs of the ANN were selected to be seven cement parameters (cement material, additive type, uniaxial compressional strength,curing pressure, curing temperature, curing time, and Young's modulus) and seventesting parameters (highest loading, loading frequency,stress increment rate, testing temperature, contain confining rock layer, confining pressure, and fatigue failure cycles). The programming work is based on R language. We looked at 150combinations of features and selected the neural net with the best performance. Back-propagation tuningwas used to fine-tuning the weights of the neural networks. The final predictoris occurrence of fatigue failure or not.

The best accuracy of the ANN method is up to 90.78%, which indicates that ANN can be an efficient method for cement sheath fatigue prediction. The fatigue model we proposed is different from previous study, because we collect the data for real wellbore geometry (hollow concentric cylindrical geometry). This makes the model more applicable to practical situation.

This paper intends to provide a more convenient and accurate model to predict cement sheath fatigue failure under cyclic pressure/ temperature change. The result will benefit the cement system design and wellbore operating strategy design such as hydraulic fracturing.
New Life for Old Offshore Platforms – Where is Geothermal an Option?
Authors: Batir Joseph F. , Gentry Emilie N. , Boak Jeremy , McDowell Bryan , Soroush Hamed
Published: April 2022

Many offshore wells and platforms are nearing abandonment, in the Gulf of Mexico, the North Sea, Offshore West Africa, and other areas. The current decommissioning process, however, is expensive. Instead of decommissioning, do these assets create an opportunity to decrease cost of decarbonization efforts using this existing infrastructure? Some platform repurposing options include geothermal energy generation (heat or electricity) using modular power generation units or pipelines back to the beach, carbon dioxide storage, hydrogen generation, and potentially others. Each option has both a technical probability of success and a financial outcome. In this study, we begin to answer the question "Where is geothermal energy an option for repurposing offshore wells and platforms?" Here, we examine the Corpus Christi Bay and the Galveston Bay for geothermal power potential as shallow water offshore platforms that will have the highest likelihood to sell power if a geothermal resource exists. Geothermal power potential is calculated and several end use scenarios for the geothermal energy, including cost of energy and project financials, are examined. Using these two regions as test cases, estimations on the potential shallow water offshore geothermal market is discussed. To perform this assessment, we examine published geothermal gradient maps and produce an industry standard geothermal resource assessment based on existing well production from the offshore platform. Using the thermal energy production, we estimate electrical power potential and total potential revenue, and then calculate high level financial metrics. Technical gaps and data gaps are assessed. CAPEX, OPEX, discounted cash flows, internal rates of return, and project lifetimes are part of the financial analysis. Indicative results are presented from the sample case studies in Corpus Christi Bay and Galveston Bay. There is no offshore geothermal energy production to date, although this is a topic of interest with significant publicity from Gulf of Mexico and North Sea operators. While there is interest, there are no public studies showing potential economics. This paper, to our knowledge, summarizes and publicly presents technical and financial parameters for shallow water offshore geothermal energy production with the specific goal to extend the life of offshore platforms and re-use existing wellbores.
Geological and Field Justification of Yuzhno-Vyintoyskoye Field Development Process Based on Dynamic Marker Monitoring in Horizontal Wells
Authors: Dulkarnaev Marat , Katashov Alexander , Belova Anna , Husein Nadir , Malyavko Evgeny , Saprykina Kseniya , Upadhye Vishwajit , Semenova Ekaterina
Published: April 2022

Effective management of oil and gas field development is impossible without employing an integrated approach to well and reservoir studies, well interference assessment, and analysis of the reservoir pressure maintenance system effectiveness. To make hard-to-recover reserves development effective, the key objective is to develop and substantiate recommendations for stimulating oil recovery and increasing the oil recovery factor.

This paper dwells on a new approach to the geological feasibility study of the field development management and inter-well influence evaluation, which involves predictive modelling. Such an approach implies studying the field geological structure, analysing the current recovery status, dynamic quantum tracer-based production profile surveillance in horizontal wells, as well as using Spearman rank correlation analysis to evaluate the performance of the reservoir pressure maintenance system.

The subject reservoir is represented by a series of wedge-shaped Neocomian sandstones that are marked by a complex geological structure, lateral continuity, non-uniform distribution of reservoir rocks, and an extensive water-oil zone. At the moment, the subject field is in a production increase cycle (Dulkarnaev et al., 2020).

An integrated approach was used in this study to provide an extra rationale to the starting points of the reservoir pressure maintenance system impact at new drilling sites to improve oil recovery and secure sustainable oil production and the reserve development rate under high uncertainty.
Experimental Research on Mechanical Properties for Hydraulic Fracture Design of Weak Sands: A Case Study in Bohai
Authors: Han Zhilei , Cui Yunjiang , Xu Sainan , Ma Chao , Cao Meng
Published: April 2022

In recent years, in order to improve the oil production of unconsolidated sand reservoirs, hydraulic fracture through the screen liner has been carried out in the Bohai oilfield. Traditional hydraulic fracture design methods usually assume the rock is elastic, while the weak sands are often nonlinear elastic rocks. This study investigates how to optimize the mechanical parameters used in the hydraulic fracture design to best approximate the rock elastic properties under in-situ formation conditions, and show how to derive them from well logs.

We performed uniaxial strain and triaxial stress compression experiments on five and seven groups of core samples, which were from well P6 and well P8, respectively. Each group of samples had five plugs with similar depth, one of which was designed for the uniaxial strain experiment and the other four for the triaxial stress experiment. Linear regression analyses and extrapolations were carried out for each set of core data to find the proper mechanical parameters for the fracturing design. The quantitative conversion formulas between core analysis and well log-derived results of these moduli were eventually established.

The reservoir rocks of P oilfield located in Bohai are relatively weak and have low stiffness. Mechanical experiments show that there is a linear correlation between deformation modulus and effective confining pressure. Core data analyses indicate that constrained modulus provides the best approximation of rock modulus under initial reservoir conditions. And its value is the ratio of stress to strain (from initial linear data) in the uniaxial strain experiment. Therefore, it is recommended to use the constrained modulus as the mechanical parameter in hydraulic fracture design. Meanwhile, there is a strong linear relationship between constrained modulus and Young's modulus, and the former is roughly six times greater than the latter. Combining the conversion between core and well log-derived Young's modulus, it is possible to estimate constrained modulus from well logs. The method proposed in this research was used for the fracturing design of well P45. After fracturing, the daily oil production increased from 399.3 to 2,462.9 ft³, and the liquid production increased four times.

Based on the studies of rock mechanics laboratory data, we propose that constrained modulus is the appropriate parameter in the hydraulic fracture design of soft sediments. And it has been verified by actual production data. The new method provides a reliable reference for the hydraulic fracture design of weakly consolidated sands in Bohai and other similar oilfields.