bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hybrid Modeling for Multiphase Flow Simulations
Authors: Henriksson Johan , Wollebæk Lars , Yang Zongchang 
Published: April 2022

Abstract
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

Abstract
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

AbstractIn 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 (&gt;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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.