bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integrated Characterization of Sand Production for Clayey-Silt Hydrate Formations by Coupling Geomechanics and Pressure Gradient-Based Sand Failure Criteria
Authors: Yurong Jin , Daoyong Yang , Yanlong Li , Nengyou Wu 
Published: September 2022

Abstract
In this work, a robust and pragmatic technique is developed to characterize the sediment deformation and sand production for clayey-silt sediments in the absence and presence of hydrate by coupling reservoir simulation and geomechanics. Such an integrated model considers the pressure gradient-based (PGB) sand failure criterion, changes in both porosity and permeability, and the three-dimensional (3D) displacement dynamics (i.e., deformation). Within the modified hydrate reservoir simulator, its geological module including displacement dynamics and changes in porosity and permeability due to deformation is solved with the staggered grid finite difference approach. Subsequently, the proposed model is validated by reproducing the experimentally measured profiles for both hydrate-free and hydrate-bearing sediments under various conditions. Excellent agreements between the measured profiles and simulation data have been achieved. It is found that, for the radial consolidation, the unconsolidated clayey-silt sediment is excessively compressed with a slight increase in external pressure (σex&lt;0.50 MPa), and then the compaction rate slows down. Consistent with the gas and water production, the sediment subsidence is also composed of three stages, i.e., before hydrate dissociation (confining stress dominated), during hydrate dissociation (both confining stress and hydrate cementing effect), and after hydrate dissociation (confining stress dominated). The numerical results show that the sediment subsidence plays a critical role in porosity variation compared with sand creeping (i.e., development of wormholes or fluidization channels), while the permeability increment from the sand creeping substantially exceeds the permeability impairment from sediment subsidence.
Forward Modeling of Natural Fractures within Volcanic Reservoir by Using Continuum Damage Mechanics
Authors: Shen Xinpu 
Published: September 2022

Abstract
By using the continuum damage mechanics’ model and finite element numerical simulation technology, this paper studies natural fractures’ distribution within volcanic reservoir and presents numerical solution of contour of damage variable which represents distribution of natural fractures within target formations. Workflow of numerical calculation was proposed. These techniques were applied to investigate natural fractures distribution generated by tectonic movement within volcanic reservoir of an oilfield in west China. Quasi-brittle plastic damage model is used to simulate fractures generated by technical movement within volcanic formations given in this region. Initial-strain-method is used to simulate loading applied to target formation caused by tectonic movement instead of using boundary displacement loading. Different values of magnitude and orientations of principal strain components have been tested in order to find their proper values. Values of parameters of damage models are calibrated by matching numerical results of natural fractures’ distribution to those obtained by interpretation of seismic data. Principal results are: (1) in the upper Triassic Xiaoquangou formation reservoir and the lower Tiaohu formation reservoir, numerical solution of natural fractures’ distribution is consistent with those obtained from interpretation of seismic data. (2) The maximum value of damage variable of the upper Triassic Xiaoquangou formation is 0.2678, i.e., the degree of crushing is 26.78%. The width of the zone of damage localization is about 10 meters to 300 meter. The maximum value of damage variable in the lower Tiaohu formation is 0.2569, which is slightly less than that in the upper Xiaoquangou formation. (3) Within the target block, zones of natural fractures are mainly distributed in central-east part of the block. Therefore the planned new wells should be located in this part of the block. Zones of natural fractures in the west part of the target block are in the shape of narrow bands which are lack of connections, and thus this west side of the block is not a good place for new wells. This case study provides a best practice for identification of natural fractures within volcanic reservoir.
Novel Deployment of Noninvasive Sensing Technology Improves Wellhead Installation
Authors: Lim Haw Keat , Hogg Bruce , Gil Alberto , Lee Yong Wee , Ozen Onur , Loviken Pontus , Kherroubi Josselin 
Published: September 2022

Abstract
Correct installation of the wellhead is crucial to drilling operations. As drilling activities come to involve increasingly critical and challenging operations, the demand for improved reliability, performance, and safety continues togrow. Hanger and packoff installations during well construction can be negatively affected by debris, misalignment, and stuck casing, either independently or in combination,leading to costly downtime. Currently, these installations are verified using manual methods (markings, visual inspections, and weight indications), posing a high potential risk for errorsas a result of human factors. The wellhead installation digital position sensing solution removes the human factor and provides assurance that the equipment is properly landed as designed.
The wellhead installation digital position sensing solution is the industry’s first digital installation advisor that provides real-time feedback to improve the reliability of wellhead equipment installation. The core technology is a set of ultrasonic transducers working in pulse-echo mode that is mechanically mounted on the wellhead housing exterior. The ultrasonic pulses sent into the housing reflect against the internal components, providing a unique signature of the internal state of the housing using anonintrusive method. The reflected signals are digitally recorded and analyzed using a binary classification network that was trained through machine learning to confirm correct installations.
The technology and algorithm were developed using extensive data collected through full-size prototype laboratory testing. A test fixture simulating a wellhead system was constructed to collect digitalsignatures of various scenarios of hanger landings and packoff settings at carefully controlled offsets from proper installations. The collected signatures were then used to train the binary classification network. The first field trial was performed on a modular compact wellhead system in collaboration with an operator in the Middle East. The digital position sensing solution performed robustly in the field with 93.5% accuracy during the hanger landing operation. The hardware prototype was deployed on two different rigs and successfully detected and confirmed the correct landing of wellhead components during all operations.
The digital position sensing solution provides cost savings to operators by reducing equipment installation time, decreasing unplanned downtime, and reducing the potential for human error. It also provides operators and field service teams with greater confidence that the wellhead equipment has been properly installed.
An Efficient Deep Learning-Based Workflow for CO2 Plume Imaging Using Distributed Pressure and Temperature Measurements
Authors: Nagao Masahiro , Yao Changqing , Onishi Tsubasa , Chen Hongquan , Datta-Gupta Akhil 
Published: September 2022

Abstract
Geologic carbon dioxide (CO2) sequestration has received significant attention from the scientific community as a response to global warming due to greenhouse gas emission. Effective monitoring of CO2 plume is critical to CO2 storage safety throughout the life-cycle of a geologic CO2 sequestration project. Although simulation-based techniques such as history matching can be used for predicting the evolution of underground CO2 saturation, the computational cost of the high-fidelity simulations can be prohibitive. Recent development in data-driven models can provide a viable alternative for rapid CO2 plume imaging. Here, we present a novel deep learning-based workflow that can efficiently visualize CO2 plume in near real-time.
Our deep learning framework utilizes field measurements, such as downhole pressure, distributed pressure and temperature as input to visualize the subsurface CO2 plume images. However, the high output dimension of CO2 plume images makes the training inefficient. We address this challenge in two ways: first, we output a single CO2 onset time map rather than multiple saturation maps at different times; second, we apply an autoencoder-decoder network to identify lower dimensional latent variables that compress high dimensional output images. The ‘onset time’ is the calendar time when the CO2 saturation at a given location exceeds a specified threshold value. In our approach, a deep learning-based regression model is trained to predict latent variables of the autoencoder-decoder network. Subsequently the latent variables are used as inputs of the trained decoder network to generate the 3D onset time image, visualizing the evolving CO2 plume in near real-time.
The power and efficacy of our approach are demonstrated using both synthetic and field-scale applications. We first validate the deep learning-based CO2 plume imaging workflow using a 2D synthetic example. Next, the visualization workflow is applied to a 3D field-scale reservoir to demonstrate the robustness and efficiency of the workflow. The monitoring data set consists of distributed temperature sensing (DTS) data acquired at a monitoring well, flowing bottom-hole pressure data at the injection well, and time-lapse pressure measurements at several locations along the monitoring well. Our approach is also extended to efficiently evaluate the uncertainty of predicted CO2 plume images. Additionally, an efficient workflow for optimizing data acquisition and measurement type is demonstrated using our deep learning-based framework.
The novelty of this work is the development and applications of a unique and efficient deep learning-based subsurface visualization workflow for the spatial and temporal migration of the CO2 plume. The efficiency and flexibility of the data-driven workflow make our approach suitable for field-scale applications.
Buoyant Flow of H2 Versus CO2 in Storage Aquifers
Authors: Ren Bo , Jensen Jerry , Duncan Ian , Lake Larry 
Published: September 2022

Abstract
Hydrogen will play an important role in the quest to decarbonize the world’s economy by substituting fossil fuels. In addition to the development of hydrogen generation technologies, the energy industry will need to increase hydrogen storage capacity to facilitate the development of a robust hydrogen economy. The required hydrogen storage capacity will be much larger than current hydrogen and natural gas storage capacities. There are a several geological storage options for hydrogen that include depleted hydrocarbon fields and aquifers, where more research is needed until the feasibility to store hydrogen at scale is proven. Here, we investigate the buoyant flow of H2 (as a working gas) versus CO2 (as a cushion gas) separately in a representative storage aquifer. Buoyant flow can affect the maximum storage, capillary trapping, likelihood of leakage, and deliverability of aquifer-stored hydrogen.
After building a two-dimensional geological reservoir model initially filled with saline water, we ran numerical simulations to determine how hydrogen placed at the bottom of an aquifer might rise through the water column. The Leverett j-function is used to generate heterogeneous capillary entry pressure fields that correlate with porosity and permeability fields. Hydrogen viscosities were based on the Jossi et al. correlation, and the density was modeled using the Peng-Robinson equation of state. We then simulated several scenarios to assess flow during short- (annually) and long- (several years) term storage. For comparison purposes, we also ran CO2 storage simulations using the same geological model but with CO2-brine-rock properties collected from the literature.
For a representative storage aquifer (323 K, 15.7 MPa, and mean permeability of 200 mD), significant fingering occurred as the hydrogen rose through the saline water column. The hydrogen experienced more buoyant flow and created flow paths with increased fingering when compared with CO2. Individual hydrogen fingers are thinner than the CO2 fingers in the simulations and the tip of hydrogen finger fronts propagated upward roughly twice as fast as the CO2 front for a typical set of heterogeneity indicators (Dykstra-Parson’s coefficient Vdp = 0.80, and dimensionless autocorrelation length λdx = 2).
The implications of buoyant flow for hydrogen in saline aquifers include an increased threat of leakage, more residual trapping of hydrogen, and, therefore, the need to focus more on the heterogeneity and lateral correlation behavior of the repository. If hydrogen penetrates the caprock of an aquifer, it will leak faster than CO2 and generate more vertical flow pathways. We identify possible depositional environments for clastic aquifers that would offer suitable characteristics for storage.
A Transmitter-Less Inspection Tool for Pipe Defect Detection and Characterization
Authors: Mostafa Tarek M. , Khater Moutazbellah , Ozakin Mehmet , Ooi Guang , Bagci Hakan , Ahmed Shehab 
Published: September 2022

Abstract
Pipe strings are commonly employed in the oil and gas sector, where they are subjected to immense strains and highly corrosive fluids. Electromagnetic (EM) based inspection tools are widely used and typically require one or more excitation sources (i.e., transmitter coil(s)), as well as sensing devices, which adds complexity and expense. By utilizing the inductive sensing principle, this work introduces a novel inspection method with no transmitter coil(s) capable of characterizing inner and full metal loss on the pipe's body. The proposed technique has been modeled and simulated using the commercial EM solver ANSYS Maxwell, as well as a proof of concept prototype, has been built. The results reveal that the tool could detect full and partial metal loss and fully characterize the defect's size, location, and depth.
Surfactant Blends for Steam Foam Applications up to 570°F (~300°C)
Authors: Wilson Daniel , Landry Thu , Morgan Carla , Stanciu Cornell , Nguyen Nhut , Nguyen Quoc 
Published: September 2022

Abstract
Steam injection with foam presence (steam foam) was successfully implemented in the 1970's and 1980's. The purpose of having steam foam as a mobility control agent was to improve sweep efficiency by minimizing detrimental effects associated with steam injection processes such as viscous fingering, gravity override, loss to thief zones, etc. Previous work, published in SPE-190473-MS and SPE-193634-MS, discussed results on thermal stability and foamability of Alkyl Ether Carboxylate Acid (ECA) at temperatures up to 200°C-250°C in the presence/absence of bitumen. This paper, as a continuation of the work mentioned above, presents the blending of ECA with surfactant A (a chemically different surfactant) at 50/50 ratio and different injection foam qualities (30% to 90%) for further improvement of steam foam performance at 300°C without bitumen.
For safety reasons, thermal stability test at 300°C using Parr reactors was not conducted because of identical rated temperature on the instrument. Surfactant foamability was evaluated through bulk foam test in a high temperature high pressure (HTHP) visual cell based on the recorded life-time of foam column. Two Internal Olefin Sulfonates (IOS) from Shell were also tested and used as benchmark for comparison with ECA itself and the blend at 250°C and 300°C. Through sand-pack tests conducted at 300°C, foamability in porous media of ECA – surfactant A blend at different injection foam qualities was compared against ECA itself for observation of improvement with regards to increasing steam apparent viscosity in the absence of bitumen.
Results showed that the presence of surfactant A improved foamability by 50% at 50/50 blend in bulk foam test. Sand-pack test results confirmed significant increase of steam appearance viscosity with the addition of surfactant A. Varying injection foam qualities resulted in 30% and 50% being the best foam qualities in terms of increasing steam apparent viscosity as compared to 70% and 90% foam qualities.
The findings of this study once again confirm the effectiveness of steam foam as a mobility control agent for steam injection processes. The addition of surfactant A to ECA improves foamability at 300°C which broadens the application of steam foam to reservoirs with similar operating temperature. Sensitivity study on injection foam qualities provides insights on process design with respect to desired foam strength and gas availability.
Application of Machine Learning for Mineralogy Prediction from Well Logs in the Bakken Petroleum System
Authors: Laalam Aimen , Boualam Aldjia , Ouadi Habib , Djezzar Sofiane , Tomomewo Olusegun , Mellal Ilyas , Bakelli Omar , Merzoug Ahmed , Chemmakh Abderraouf , Latreche Abdeljallil , Berrehal Badr Eddine 
Published: September 2022

Abstract
One of the significant unconventional oil reserves in the USA is the Bakken Petroleum System located in the Williston Basin. It is known for its complex lithology, composed of three prominent members, Upper and Lower Bakken, with similar properties of organic-rich shale relatively uniform compared to the middle member with five distinct lithofacies, formed mainly from calcite, dolomite, or silica. The higher properties variability makes the reservoir characterization more challenging with low permeability and porosity. Understanding lithology by quantifying mineralogy is crucial for accurate geological modeling and reservoir simulation. Besides that, the reservoir's capacity and the oil production are affected by the type and the mineral volume fractions, which impact the reservoir properties. Conventionally, to identify the mineralogy of the reservoir, the laboratory analysis (X-Ray Diffraction, XRD) using core samples combined with the well logs interpretation is widely used. The unavailability of the core data due to the high cost, as well as the discontinuities of the core section of the reservoir due to the coring failures and the destructive operations, are one of the challenges for an accurate mineralogy quantification. The XRD cores analysis is usually used to calibrate the petrophysical evaluation using well logs data because they are economically efficient. To remedy to these limitations, artificial intelligence and data-driven based models have been widely deployed in the oil and gas industry, particularly for petrophysical evaluation. This study aims to develop machine learning models to identify mineralogy by applying six different machine learning methods and using real field data from the upper, middle, and lower members of the Bakken Formation. Efficient pre-processing tools are applied before training the models to eliminate the XRD data outliers due to the formation complexity. The algorithms are based on well logs as inputs such as Gamma Ray, bulk density, neutron porosity, resistivity, and photoelectric factor for seven (07) wells. XRD mineral components for 117 samples are considered outputs (Clays, Dolomite, Calcite, Quartz, and other minerals). The results' validation is based on comparing the XRD Data prediction from the developed models and the petrophysical interpretation. The applied approach and the developed models have proved their effectiveness in predicting the XRD from the Bakken Petroleum system. The Random Forest Regressor delivered the best performance with a correlation coefficient of 78 percent. The rest of the algorithms had R-scores between 36 and 72 percent, with the linear regression having the lowest coefficient. The reason is the non-linearity between the inputs and outputs.
Challenging Standard Practices: How a Review of Standard Practices Resulted in an Improvement in BHA Reliability
Authors: Marland Christopher , Gonzalez Duane , Koy James 
Published: September 2022

Abstract
Bottom Hole Assembly (BHA) reliability is critical to deliver wells on-time and within budget. Improving reliability is a combined effort between operators and service providers. In the D.J. Basin, well drilling times are between 48-60hours. A 10-hour trip for failure can account for over 20% of well time. Onshore operations have benefited from focusing on performance metrics. To achieve some metrics, drilling procedures have been implemented that may have had unintended consequences. It will be shown how a review and change to standard drilling practices helped improve BHA reliability
BHA failures occurred on 35% of wells in the operating area of this project. Although steps were taken, such as third-party inspections of tool builds, trialing various BHA components, and adjusting drilling parameters, failure still occurred. Failures were split between motors, measurement while drilling (MWD) and rotary steerable system (RSS) tools. Motor chunking was present on most BHAs, regardless of vendor. Chunking either caused motor failure or blocking of MWD or RSS components despite filters downstream of the motor. A review of operating practices with the service provider concluded that modifications could reduce on-bottom and off-bottom BHA loads and improve reliability. A series of procedural changes were made to connection practices, back-to-bottom procedures and downlink practices and the results tracked.
Increased motor reliability was the main goal and would be determined by post-run observations of chunking evidence. A secondary aim was to reduce other failures that may be caused by mechanical overload. After implementing the procedural changes, the first 11 wells were completed in a single run, with no non-productive time (NPT) for BHA failures and no motor rubber observed in filter subs. Several wells were also able to re-run the RSS resulting in an invoice credit and further cost savings. Although the changes have resulted in a small increase in connection time per well, the pad level time savings from improved BHA reliability offsets that cost. There is also greater confidence that the applied drilling parameters start from a zero-load point. This allows the operator and service company to have more confidence that downhole tools are not being overloaded and gives more opportunity to optimize drilling parameters for performance.
This paper will challenge standard practices that target incremental performance gains but may result in seemingly unrelated BHA reliability problems. Modeling drillstring stretch and understanding BHA load conditions has resulted in reliability improvements for the basin that can be applied to similar applications for the operator and the industry.