bluuebunny/crossref_metadata_2025_split huggingface dataset:
Real Time Artificial Intelligence Assisted Operations Geology: Lithology and Fluids Early Identification and Automatic Well Correlation
Authors: Di Palo Arcangelo , Pianaro Stefano , Crottini Ada , Tempone Pamela , Sanasi Carla 
Published: January 2023

Abstract
During the drilling of a well, a huge quantity of data is acquired in real-time. In order too mitigate risks due to geological uncertainties, to increase operational efficiency, to optimize processes and create new business models, Eni has developed its own cross-functional integrated data platform, which ensures data availability to all subsurface technical functions sharing a common data model. In this paper we describe an innovative approach, born from the collaboration between expert geologists and data scientists. The integrated team has developed a tool based on Artificial Intelligence (AI) supporting operations geologist during drilling phases. Two different tools have been created: litho-fluid interpretations, a set of AI algorithms used to identify in real-time the lithology and to interpret the formation fluids; well-to-well log correlation and look ahead, models used to find analogies between intervals of the well being drilled and the reference well, allowing to estimate the distance and time of arrival to a given geological event.
The results obtained have been remarkable in terms of accuracy. The positive feedbacks from the operations geologists give the assurance of the usefulness of the tools and their expected benefits: the tools allow to better control geological uncertainties and speed up some repetitive and time-consuming tasks. The results presented in this paper are focused on two UAE applications of litho-fluid and well-to-well log correlations.
Machine Learning Assisted Flash Calculation for Sour Gas and Crude Oil
Authors: Koyanbayev Madiyar , Wang Lei , Zhumatai Aset 
Published: January 2023

Abstract
Compositional modeling is an effective and necessary technique for designing and optimizing complex oil and gas production processes, such as gas injection enhanced oil recovery (EOR). Generally, about 70% of computational time in compositional modeling is consumed by flash calculation. Replacing iterative flash calculations partially or completely in obtaining the satisfactorily accurate number of phases and compositionswith machine learning (ML) models is proven to be an efficient strategy to accelerate flash calculation. In this study, support vector machine (SVM), artificial neural network (ANN), and decision trees (DT) models were trained, validated, and tested based on 10,000 PVT data sets, which were artificially generated for a sour crude oil sample containing 13.32 mol% H2S and 2.9 mol% CO2. Comparative analysis showed that all three ML models could be used to make an accurate prediction. The obtained ANN model was compared with the vapor–liquid equilibrium (VLE) calculation and was used as a surrogate model for accelerating the flash calculation. The comparison revealed that the ANN model reduced the computational time of the VLE calculation by as high as 250 times. Moreover, the phase diagram of sour crude oil obtained by the ANN model was close to the phase diagram generated by VLE, which proves the accuracy and robustness of the ANN model. Overall, this work shows that ANN model is effective in accelerating flash calculation and reducing the computational time of compositional reservoir simulation.
A Detail Study Evaluating the Impact of Downhole and Wellhead Compression to Optimize Production From Gas Wells with Liquid Loading Issue: An ADNOC Onshore Gas Field Case Study
Authors: Bernadi Bondan , M. Douglas Mahmoud , Al Shamsi Alia Belal Zuwaid Belal , Omar Alawadhi Fatima , Nabil Mohamed Ihab , Ahmed Hosani Mariam , Mohamed Al Bairaq Ahmed , Abdullayev Azer , Roopal Allen 
Published: January 2023

Abstract
An onshore retrograde gas field in Abu Dhabi has several wells with severe liquid loading issue as reflected by low intermittent production rates. To overcome this, a study was performed by looking at the effectiveness of downhole and wellhead compression and their impact on the production rates. Utilizing IAM, the benefit will not only be seen at the well level but also at the field scale with the ultimate goal to increase the Recovery Factor.
The study was started by identifying the wells suffering from liquid loading issue. The wells were then ranked according to the severity of the problem based on the GLR values. Hence, the top 2 wells were prioritized as the candidates. Both the downhole and wellhead compressors were then modelled in IAM and several sensitivity runs were performed to evaluate the optimum compressor settings to see the impact on flow stabilization and wells productivity. The selected best scenarios were also compared with the base case as reference where no intervention was performed at all (natural flow).
The study reveals that both downhole and wellhead compression can give wells flowrate stability with substantial improvement on the production rates for longer duration of time compared to the base case scenario. More than that, the downhole compression shows better gain for production rates compared to the wellhead compression. This is mainly attributed to the fact that gas density is higher at the bottom of the well than at the surface and since the mass flow rate depends on the density of the gas and the volumetric flow rate, this will lead to higher mass flow rate for the same volumetric flow. Consequently, a downhole compressor will produce more fluid quantity rather than a wellhead compressor.
The study clearly demonstrates and evaluates the effectiveness of downhole and wellhead compressors to mitigate liquid loading inclusively to improve gas wells production. By utilizing IAM that is capable to capture the interactions between subsurface to surface network elements, the improvements on wells deliverability after implementing downhole and/or wellhead compression can be accounted more accurately taking into account more complex production operations with the ultimate goal to maximize the field Recovery Factor.
Unveiling Reservoir Architecture and Characterization Using Ultra-HD Bed Boundary Mapper
Authors: Saleh Abdullah S. , Sultan Shehri A. , Santoso Gagok Imam , Alshammari Huliayan Furreh 
Published: January 2023

Abstract
Drilling a well in one specific layer is challenging. In the subject well, multiple zones have been determined in the target reservoir. Each zone has its own property and distribution. The objective of this paper is to unveil the reservoir architecture and formation characterization using the latest logging-while-drilling (LWD) technology in ultra-high definition boundary orientation.
The methods presented in this paper are to integrate field geological knowledge, wellbore centric LWD data (Images and Triple Combo) and Ultra High Definition Bed Boundary Mapper that extend EM measurements, which allows a better fine-tuning structure and formation resistivity interpretation around the wellbore, independent of mud type. Along with those measurements, a new resistivity inversion has been developed that computes geometrically corrected resistivity for both vertical Rv, and horizontal Rh from bed boundary locations, and formation relative dip. In addition, formation relative dips and boundary positions from the inversion are in good agreement with relative dips from other sources such an image analysis.
The tool deployed successfully and enabled optimum reservoir exposure in a good porosity zone. Along the boundary orientation for geosteering purposes, the technology was able to map another two porous zones within a 20 ft depth of the investigation. The qualitative formation resistivity, both Rh and Rv, were also used for formation evaluation in this reservoir at the well location, which brought the robust information value, especially in high angle and horizontal (HA/HZ) wells because of the complicated tool responses when crossing boundaries. Deep directional EM tools are beneficial for inversions to derive formation properties since they provide more measurements and information about the formation resistivity without the need to cross layer boundaries. Data validation done through several validations by comparing the RhRv value with the vertical well resistivity. Other than that, we also did a general approach for solving formation parameters to search through all possible model scenario and find the one that best matches the actual 2 Mhz 28 and 16-in. coaxial propagation responses using a 2-layer model.
Initial run results are very encouraging, demonstrating the value to improve efficiency in reservoir characterization and interpretation. The technology enables better understanding the reservoir architecture and formation evaluation through several application from single LWD measurement.
Fluid Monitoring to Optimize GOGD Recovery in Highly Fractured Reservoir Field in North of Sultanate of Oman
Authors: Al Hussaini Azza , Al Nofli Kholood , Agarwal Kavita , Abri Mustafa 
Published: January 2023

Abstract
Gas oil Gravity Drainage (GOGD) process is one of the highly efficient recovery mechanism in heavily fractured carbonate reservoirs. The driving force is the gravity, the oil is moving down from the matrix block through fractures until it reaches the producers. To ensure optimum offtake from the reservoir through the horizontal wells the oil rim need to be stabilized and very well managed through the gas injection, offtake/intake balance, aquifer pump off.
A key challenge of optimizing the gas injection as well as the offtake from the horizontal wells to achieve effective GOGD recovery is to ensure that a minimum oil rim thickness is maintained, and that oil rim is kept at the same depth as the oil production well. If not;
When the oil rim moves down, the well will produce gas instead of oil, therefore resulting in deferment and inefficient GOGD recovery. When the oil rim moves up, the well will produce water from the fracture system, therefore resulting in deferment and significant time to recover the oil rim to the correct depth.
A typical monitoring method for the fracture fluid fill is to run "gradio" surveys in dedicated observation wells to measure the fGOC and fOWC (where "f" represent the fracture system contacts i.e. fractured gas oil contact and fractured oil water contact rather than the matrix contacts). Also the fluid movement in matrix can be monitored by Pulsed-Neutron logging, in combination with Open-hole logs that have been acquired at different times through the development.
In addition to the logging methods, GOGD Flow Unit characterization was introduced to depict the effect of the intake/offtake changes and oil rim movement within the GOGD system along with a surveillance strategy focusing on reservoir optimization. Fluid contact visualization plots were generated for each flow unit which give a clearer picture in how effective is the current GOGD system and the future reservoir development and optimization.
More than 5 flow units with almost 150 active wells,3 gradio wells and 5 pulsed neutron log data were reviewed, to generate fluid contact visualization plots and justify flow unit production behavior. This analysis initiative helped to depict the effect of the intake/offtake changes and oil rim movement within the GOGD system. This work also led to adding additional development opportunities like placement of future new wells or adding perfs /carrying out zone change in existing closed in wells either to produce oil or to pump off water. This eventually fed into the reservoir optimization and surveillance strategy.
Deep Learning-Based and Kernel-Based Proxy Models for Nonlinearly Constrained Life-Cycle Production Optimization
Authors: Atadeger Aykut , Onur Mustafa , Sheth Soham , Banerjee Raj 
Published: January 2023

Abstract
In this study, we investigate the use of deep learning-based and kernel-based proxy models in nonlinearly constrained production optimization and compare their performances with directly using the high-fidelity simulators (HFS) for such optimization in terms of computational cost and optimal results obtained. One of the proxy models is embed to control and observe (E2CO), a deep learning-based model, and the other model is a kernel-based proxy, least-squares support-vector regression (LS-SVR). Both proxies have the capability of predicting well outputs. The sequential quadratic programming (SQP) method is used to perform nonlinearly constrained production optimization. The objective function considered here is the net present value (NPV), and the nonlinear state constraints are field liquid production rate (FLPR) and field water production rate (FWPR). NPV, FLPR, and FWPR are constructed by using two different types of proxy models. The gradient of the objective function as well as the Jacobian matrix of constraints are computed analytically for the LS-SVR, whereas the method of stochastic simplex approximated gradient (StoSAG) is used for optimization with E2CO and HFS. The reservoir model considered in this study is a two-phase, three-dimensional reservoir with heterogeneous permeability which is taken from the SPE10 benchmark case. Well controls are optimized to maximize the NPV in an oil-water waterflooding scenario. It is observed that all proxy models can find optimal NPV results like optimal NPV obtained by HFS with much less computational effort. Among proxy models, LS-SVR is found to be less computationally demanding in the training process. Overall, both proxy models are orders of magnitude faster than numerical models in the prediction. We provide new insights into the accuracy and prediction performances of these machine learning-based proxy models for 3D oil-water systems as well as their efficiency in nonlinearly constrained production optimization for waterflooding applications.
Does it Have to be Complex? Pragmatic Approach to Developing Newly Discovered Naturally Fractured Reservoir
Authors: Suut Nur Suria Amalia , Shukri Khalsa , Borbajo Renato 
Published: January 2023

Abstract
Located in remote interior South of Oman, Field X historically comprised of multi-stacked clastic reservoirs. Since its production in 1980s, production had declined significantly that the field was identified as a potential abandonment candidate. In 2020, Field X integrated team successfully discovered commercial oil accumulation in overburden, naturally fractured carbonates reservoir Y; first carbonates discovery in the area. This case study focused on how the team embarked on the challenge to accelerate production from this new reservoir. Following this success, short-term opportunities were identified whereby existing vertical well stocks were re-perforated to evacuate production from reservoir Y.
Key decisions that needed to be answered for full field development concept were optimum number of wells, well type and recovery mechanism. Analogues study was done and horizontal appraisal wells were identified and drilled. Acquisition and interpretation of static (including image logs) and dynamic (including pressure transient analysis) data in reservoir Y provided somewhat fractures characterization. A model framing event was conducted to identify complexity level of dynamic modeling required to answer above key decisions. History matching and sensitivity analysis using experimental design was performed to identify the most impacting key parameters to the development. Reperforation of existing vertical well stocks and 2021 horizontal well appraisal drilling not only generated early oil, but also valuable data gathering which helped in determining uncertainties ranges. Sensitivity analysis concluded that key uncertainties to the project were fracture characterization and its distribution across the field and oil water contact. Decision-based modeling approach was carried out. This means rather than building complex 3D-discrete fracture network model, a relatively simple full-field 3D-Dual Permeability simulation was deemed sufficient to answer decisions to be made whilst honoring uncertainty ranges. This was not only efficient but also at the same time brought early insights to key uncertainties in the development and therefore the team was able to identify what additional data acquisitions are relevant for the project moving forward. Three deterministic realisations were selected from probabilistic runs, and forecast results were also benchmarked to analogous fields for realism checks.
The development will be phased, with high confidence wells drilling scheduled to start in early 2023, to balance between early oil recovery (first key value driver of the project), lower UTC and maximising ultimate recovery. The total development of reservoir Y, up to 5 wells drilling campaign, was estimated to bring total resources volume double of that of NFA's (no further activities) remaining reserves. This leads to growth for Field X with attractive UTC and NPV and ultimately maximizing its value.
Physics Informed Surrogate Model Development in Predicting Dynamic Temporal and Spatial Variations During CO2 Injection into Deep Saline Aquifers
Authors: Tariq Zeeshan , Yan Bicheng , Sun Shuyu 
Published: January 2023

Abstract
Geological Carbon Sequestration (GCS) in deep geological formations, like saline aquifers and depleted oil and gas reservoirs, brings enormous potential for large-scale storage of carbon dioxide (CO2). The successful implementation of GCS requires a comprehensive risk assessment of the confinement of plumes at each potential storage site. The accurate prediction of the flow, geochemical, and geomechanical responses of the formation is essential for the management of GCS in long-term operations because excessive pressure buildup due to injection can potentially induce fracturing of the cap-rock, or activate pre-existing faults, through which fluid can leak. In this study, we build a Deep Learning (DL) workflow to effectively infer the storage potential of CO2 in deep saline aquifers. Specifically, a reservoir model is built to simulate the process of CO2 injection into deep saline aquifers, which considers the coupled phenomenon of flow and hydromechanics. Further, the reservoir model was sampled to account for a wide range of petro-physical, geological, and operational parameters. These samples generated a massive physics-informed simulation database (about 1500 simulated data points) that provides training data for the DL workflow. The ranges of varied parameters were obtained from an extensive literature survey. The DL workflow consists of Fourier Neural Operator (FNO) to take the input of the parameterized variables used in the simulation database and jointly predict the temporal-spatial responses of pressure and CO2 saturation plumes at different periods. Average Absolute Percentage Error (AAPE) and coefficient of determination (R2), Structural similarity index (SSIM), and Peak Signal to Noise Ratio (PSNR) are used as error metrics to evaluate the performance of the DL workflow. Through our blind testing experiments, the DL workflow offers predictions as accurate as our physics-based reservoir simulations, yet 300 times more efficient than the latter. The developed workflow shows superior performance with an AAPE of less than 5% and R2 score of more than 0.99 between actual and predicted values. The workflow can predict other required outputs that numerical simulators can typically calculate, such as solubility trapping, mineral trapping, and injected fluid densities in supercritical and aqueous phases. The proposed DL workflow is not only physics informed but also driven by inputs and outputs (data-driven) and thus offers a robust prediction of the carbon storage potential in deep saline aquifers with considering the coupled physics and potential fluid leakage risk.
CYP2B6 genetic variation in cyclophosphamide metabolism and hemorrhagic cystitis in Fanconi anemia patients undergoing allogeneic hematopoietic cell transplantation
Authors: Ferdjallah Asmaa , Long Susie , DeFor Todd , Hoffmann Cody , Wagner John , Jacobson Pamala , MacMillan Margaret 
Published: January 2023

Abstract
Background
Fanconi anemia (FA) is an inherited disorder characterized by congenital malformations, bone marrow failure and malignancies. Hematopoietic cell transplant (HCT) is the only proven cure for the hematological complications. FA patients have increased chromosomal instability and aberrant DNA repair and thus can only tolerate low doses of chemotherapy or radiation as part of conditioning prior to HCT. Yet, they are still prone to severe regimen related toxicities (RRT) including hemorrhagic cystitis (HC) from cyclophosphamide (CY).
Objective
As CYP2B6 is a primary enzyme responsible for the catalyzation of the prodrug form of CY, understanding the association between CYP2B6 genetic variants and HC in FA patients may predict which patients will be more susceptible to developing HC.
Study Design:
A descriptive genetic association study was performed to identify genetic variants associated with HC in patients with FA who underwent HCT between 1999 and 2017. All patients received a CY-based preparative regimen and had pretransplant recipient DNA available for genomic analysis.
Results
Forty FA pediatric patients were eligible for this analysis. They had received HCT from matched sibling donors (n = 6) or alternative donors (n = 34) for marrow failure (n = 38) or myelodysplastic syndrome (n = 2). The incidence of HC was 32.5% which occurred at a median of 32 days (range 20–180) after HCT. Nine patients had a concomitant viral infection (BK virus, n = 8 both adenovirus and BK virus, n = 1). No genetic variants were significantly associated with HC. The top variants were rs2279343 (g.23060A > G), and rs2279344 (g.23280G > A) in the CYP2B6 gene. The incidence of HC among FA patients with the rs2279343 variant was 42% (CI 22–62%) compared to 20% (CI 0–40%) among those without the variant (p = 0.19). The incidence of HC among patients with the variant in rs2279344 was 40% (CI 22–58%) compared to 10% (CI 0–28%) among those without (p = 0.11).
Conclusion
No variants in our analysis were statistically associated with HC. The data suggest that CYP2B6 variants may increase the risk for HC in FA patients who received a CY based preparative therapy but these risk variants must be further evaluated in a larger population.
Challenges of Consumer Culture and Solid-waste to the Environment Protection in Pokhara Metropolitan City, Nepal
Authors: Subedi Shukra Raj , Poudel Jiban M , Subedi Madhusudan , Sharma Bigyan , Subedi Sunil , Neupane Bhim P , Lamichhane Basanta L , Neupane Badri N , Bohara Krishna K , Shrestha Gopi L 
Published: January 2023

Abstract

Changing consumer culture and increasing solid-waste have become global environmental issues leading to myriad challenges for management, especially in urban spaces. Against this backdrop, this study explores the challenges of solid-waste attached to changing consumer culture in the context of Pokhara, an urbanized tourist city in Nepal. The study has followed the qualitative research design generated through primary and secondary data. For this purpose, five informants have selected by purposive sampling and gathered their experiences, knowledge, and understanding of the relationship between changing consumer culture and increased solid-waste through in-depth interviews. The findings reveal that the leading cause of the environmental problem in this area is due to changing consumer culture along with rapidly growing urbanized cities and an increased attachment towards readymade items, which are in forceful demand with the stretching of the global market. It leads to an increase in solid-waste, and now it is a burning issue for protecting the environment in this area. Hence, consumer culture and solid-waste have interconnected aspects and contributing factors to an environmental problem in Pokhara.