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Collaborating Authors
University of Oklahoma
Applications of Machine Learning Methods to Predict Hole Cleaning in Horizontal and Highly Deviated Wells
Mendez, Michael (University of Oklahoma) | Ahmed, Ramadan (University of Oklahoma (Corresponding author)) | Karami, Hamidreza (University of Oklahoma) | Nasser, Mustafa (Qatar University) | Hussein, Ibnelwaleed A. (Qatar University) | Garcia, Sergio (University of Oklahoma) | Gonzalez, Andres (University of Oklahoma)
Summary Machine learning (ML) has become a robust method for modeling field operations based on measurements. For example, wellbore cleanout is a critical operation that needs to be optimized to enhance the removal of solids to reduce problems associated with poor hole cleaning. However, as wellbore geometry becomes more complicated, predicting the cleaning performance of fluids becomes more challenging. As a result, optimization is often difficult. Therefore, this research focuses on developing a data-driven model for predicting hole cleaning in deviated wells to optimize drilling performance. More than 500 flow loop measurements from eight studies are used to formulate a suitable ML model to forecast hole cleanout in directional wells. Measurements were obtained from hole-cleaning experiments that were conducted using different loop configurations. Experiments ranged in test-section length from 22 to 100 ft, in hole diameter from 4 to 8 in., and in pipe diameter from 2 to 4.5 in. The experiments provided measured equilibrium bed height at a specific flow rate for various fluids, including water-based and synthetic-based fluids and fluids containing fibers. Several relevant test parameters, including fluid and cutting properties, well inclination, and drillstring rotation speed (drillpipe rev/min), were also considered in the analysis. The collected data have been analyzed using the Cross-Industry Standard Process for Data Mining. This paper is unique because it systematically evaluates various ML models for their ability to describe hole cleanout processes. Six different ML techniques: boosted decision tree (BDT), random forest (RF), linear regression, multivariate adaptive regression spline (MARS), neural networks, and support vector machine (SVM) have been evaluated to select the most appropriate method for predicting bed thickness in a wellbore. Also, we compared the predictions of the selected ML method with those of a mechanistic model for cases without drillstring rotation. Finally, using the ML model, a parametric study has been conducted to examine the impact of various parameters on the cleanout performance of selected fluids. The results show the relative influence of different variables on the prediction of cuttings bed. Accordingly, flow rate, drillpipe rev/min, and fluid behavior index have a strong impact on dimensionless bed thickness, while other parameters such as fluid consistency index, solids density and diameter, fiber concentration, and well inclination angle have a moderate effect. The BDT algorithm has provided the most accurate prediction with an R of 92%, a root-mean-square error (RMSE) of 0.06, and a mean absolute error (MAE) of roughly 0.05. A comparison between a mechanistic model and the selected ML technique shows that the ML model provided better predictions.
- Europe (1.00)
- Asia > Middle East (1.00)
- North America > United States > Texas (0.68)
- Research Report > New Finding (1.00)
- Research Report > Experimental Study (0.88)
Utilizing Machine Learning to Improve Reserves Estimation and Production Forecasting Accuracy
Sharma, Ashutosh (University of Oklahoma) | Gupta, Ishank (Georgia Institute of Technology) | Phi, Thai (Georgia Institute of Technology) | Ashesh, Shubhranshu (Georgia Institute of Technology) | Kumar, Ram (Georgia Institute of Technology) | Borgogno, Fabio German (WellKnows SAS)
Abstract Crude oil is crucial for economic growth, therefore accurate production estimation is vital, especially for unpredictable US shale reservoirs. Shale production depends on kerogen quality, maturity, fracture stimulation, and reservoir properties. Nano-Darcy permeability poses challenges, impacting oil properties over time and complicating forecasting. Decline Curve Analysis (DCA) is a common but, alone it may not be accurate, resulting in under- or over-estimation of Ultimate Recovery. Numerical simulations struggle due to shale complexity, slow convergence from nano-Darcy permeabilities, and small grid size. This paper combines DCA and machine learning to forecast crude oil production, analyzing US shale data to capture intricate relationships between reservoir parameters and production decline. The dataset has over 1000 wells from a US shale gas play, with monthly production (up to first 12 months), GOR, WC, and key drivers (depth, porosity, velocity, modulus, proppant, rate, stages, ISIP). Workflow starts with DCA Arps method. Rigorous DCA fitting obtains 3 parameters: initial rate, b-factor, decline rate. ML regression models (MLR, RFR, KNN, XGBoost and SVM) have been built to predict DCA parameters as a function of key reservoir parameters. Error metrics (RMSE, MAPE, R) help in selecting the best model. A hybrid DCA-ML approach helps develop consistent forecasting methodology and generate reliable forecasts based on reservoir properties. Exploratory data analysis (EDA) handles outliers and missing values for a representative dataset. Production data was cleaned through a streamlined workflow for reliability. Cleaned data, fitted with DCA, b-factors, decline rates, etc., showed strong correlations with reservoir properties. Smaller ISIP values meant faster decline rates and smaller EURs, while higher GORs correlated with higher b-factors. Machine learning effectively characterized well production by correlating it with reservoir properties. The XGBoosting model with 3-segmented ARPS DCA proved best for forecasting with R exceeding 70% for high accuracy. Hypertuning and Monte-Carlo cross-validation avoided overfitting. This study can be used to enhance reserves estimation and production forecasting using ML to identify correlations between reservoir properties and DCA parameters. The objective is to reduce uncertainty by establishing meaningful relationships with production drivers. The workflow handles noisy production data, identifies critical drivers, analyzes trends, and generates accurate DCA guidelines for forecasting shale wells.
- Geophysics > Seismic Surveying (0.46)
- Geophysics > Borehole Geophysics (0.46)
Maintaining Petroleum Engineering Education To Support the Energy Mix of the Future
Fahes, M. (Mewbourne School of Petroleum and Geological Engineering at the University of Oklahoma) | Hosein, R. (University of the West Indies (UWI) in Trinidad and Tobago) | Zeynalov, G. (Baku Higher Oil School) | Karasalihovic Sedlar, D. (University of Zagreb, faculty of Mining, Geology, and Petroleum Engineering) | Srivastava, M. (ADNOC Offshore) | Swindell, G. S. (Consulting reservoir engineer) | Kokkinos, N. C. (International Hellenic University (IHU), Greece) | Willhite, G. P. ((deceased) former Ross H. Forney Distinguished Professor of Chemical and Petroleum Engineering at the University of Kansas) | Snyder, L. A. (University of Oklahoma)
Once petroleum engineering was formally established as a degree program, the scope and content of curricula has been evolving to match technological advancements and soft skills requirements to meet the needs of the oil and gas industry. The cyclical nature of the industry has always presented a challenge to the enrollment and the viability of the degree. However, while having to tackle challenges relating to record-low enrollment over the past few years that is the direct result of the recent industry downturn, academic programs are also experiencing mounting pressure that is transforming the core identity of these programs. Much of that pressure can be directly linked to the heightened awareness around the impact of emissions on climate and the environment. While technological advancements in clean energy is contributing to an accelerated transition in the energy sector that is reducing the share of the energy market need fulfilled by fossil fuels, all of the projections point to oil and gas continuing to be a critical part of the energy market until at least 2050. When considering development and utilization of capture technology, the share of oil and gas in the energy market could continue to remain significant well beyond 2050. Meeting this demand requires maintaining high-quality petroleum engineering educational programs, hosted in thriving academic departments, educating a steady supply of a trained workforce with both undergraduate and advanced degrees. In addition, it requires diversifying the curriculum to continue to meet the ever-changing skillset needs of the energy industry and the requirements set by the Accreditation Board of Engineering and Technology (ABET). At the same time, the curriculum should continue to support the needs for a general education that targets a well-rounded graduate that is ready to be a critical thinker and an active participant in society, while also attempting to appeal to the new generations of college students and instilling in them the principles needed to secure the safe, sustainable, just, and responsible energy industry of the future. Petroleum engineering faculty are at the forefront of these challenges, trying to balance multiple competing demands while keeping up research and educational programs that continue to be relevant. In this article, we bring to light the perspective of the experts, across the globe, that over the past 5 decades educated and prepared the workforce that supported the oil and gas industry. These perspectives were collected using a survey probing what faculty members are experiencing regarding the impact of the energy transition on recruiting students and on strategic directions in academic programs, the impact on their ability to fund their research and train graduate students, and what the faculty need to maintain a curriculum that is relevant to the future careers of their students.
- North America > United States (1.00)
- Asia > Middle East (0.95)
- Europe (0.70)
- Questionnaire & Opinion Survey (0.69)
- Personal (0.47)
- Energy > Oil & Gas > Upstream (1.00)
- Education > Educational Setting > Higher Education (1.00)
The La Luna Formation, Venezuela: A prospective unconventional reservoir
Liborius-Parada, Andreina (University of Oklahoma) | Philp, R. Paul (University of Oklahoma) | Slatt, Roger (University of Oklahoma)
Abstract Since the early 2000s, the exploitation of unconventional reservoirs has become very important to the oil and gas industry because of their high potential source of energy and economic value. Venezuela possesses a world-class hydrocarbon source rock in one of the most prolific hydrocarbon basins in the world, namely the Cretaceous La Luna Formation in the Maracaibo Basin. Outcrop and core samples collected from the northwestern Maracaibo Basin provide the database for this study. A comprehensive multiscale characterization of the samples is undertaken to unravel the stratigraphic properties of the petroleum system. In addition, a geochemical approach is taken to evaluate the prospectivity of the La Luna Formation as an unconventional resource in the Maracaibo Basin. Rock-Eval pyrolysis and biomarker data indicate that the La Luna Formation is dominated by type II kerogen, indicating an oil-prone marine organic matter origin. Total organic carbon values range between 3.85 wt% and 9.10 wt%. Distributions of isoprenoids, steranes, and terpanes including gammacerane and monoaromatic steroid hydrocarbons indicate a hypersaline, marine carbonate anoxic depositional environment. Thermal maturity parameters indicate that most of the cores are currently in the oil window. This combined stratigraphic geochemical study indicates that the La Luna Formation has excellent potential as an unconventional reservoir for oil and gas in the study area.
- Phanerozoic > Cenozoic (1.00)
- Phanerozoic > Mesozoic > Jurassic (0.92)
- Phanerozoic > Mesozoic > Cretaceous > Upper Cretaceous (0.67)
- South America > Venezuela (0.99)
- South America > Colombia > Middle Magdalena Basin > La Luna Shale Formation (0.99)
- South America > Colombia > Aguardiente Formation (0.99)
- (17 more...)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Shale gas (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Geochemical characterization (1.00)
The La Luna Formation, Venezuela: A prospective unconventional reservoir
Liborius-Parada, Andreina (University of Oklahoma) | Philp, R. Paul (University of Oklahoma) | Slatt, Roger (University of Oklahoma)
Abstract Since the early 2000s, the exploitation of unconventional reservoirs has become very important to the oil and gas industry because of their high potential source of energy and economic value. Venezuela possesses a world-class hydrocarbon source rock in one of the most prolific hydrocarbon basins in the world, namely the Cretaceous La Luna Formation in the Maracaibo Basin. Outcrop and core samples collected from the northwestern Maracaibo Basin provide the database for this study. A comprehensive multiscale characterization of the samples is undertaken to unravel the stratigraphic properties of the petroleum system. In addition, a geochemical approach is taken to evaluate the prospectivity of the La Luna Formation as an unconventional resource in the Maracaibo Basin. Rock-Eval pyrolysis and biomarker data indicate that the La Luna Formation is dominated by type II kerogen, indicating an oil-prone marine organic matter origin. Total organic carbon values range between 3.85 wt% and 9.10 wt%. Distributions of isoprenoids, steranes, and terpanes including gammacerane and monoaromatic steroid hydrocarbons indicate a hypersaline, marine carbonate anoxic depositional environment. Thermal maturity parameters indicate that most of the cores are currently in the oil window. This combined stratigraphic geochemical study indicates that the La Luna Formation has excellent potential as an unconventional reservoir for oil and gas in the study area.
- Phanerozoic > Cenozoic (1.00)
- Phanerozoic > Mesozoic > Jurassic (0.92)
- Phanerozoic > Mesozoic > Cretaceous > Upper Cretaceous (0.67)
- South America > Venezuela (0.99)
- South America > Colombia > Middle Magdalena Basin > La Luna Shale Formation (0.99)
- South America > Colombia > Aguardiente Formation (0.99)
- (17 more...)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Shale gas (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Geochemical characterization (1.00)
Embedding Existing Pipelines in Design of CO2 Transportation Networks for Optimal Sequestration Economics
Nnamdi, D. (University of Oklahoma) | Moghanloo, R. G. (University of Oklahoma)
Abstract This paper presents a method for incorporating existing pipelines into the design of optimal CO2 transportation networks for carbon sequestration projects. The selection of the optimal pipeline transportation network is a crucial aspect of large-scale carbon sequestration projects as it greatly affects the project's economics. The method proposed in this paper aims to address the limitation of existing open-source tools such as SimCCSwhich are unable to accommodate existing pipelines in techno-economic optimization. With the recent amendment to the 45Q laws, which now offers 70% more tax credits for carbon sequestration than in 2018, energy companies are exploring the possibility of repurposing gas and liquid transportation lines for CO2 transportation to abandoned oil and gas wells for carbon sequestration. This has further reinforced the need for a method that accounts for existing pipelines in sequestration economics. The proposed method achieves this by representing the pipeline paths on the construction cost graph as zero-cost paths. Additionally, pipeline tie-in locations are fixed by creating exclusion zones that limit inflow edges around the pipeline path. The solution is then obtained by solving for candidate transportation network routes using graph shortest path algorithms. This reformulation of the CO2 source-sink connection flow problem with limiting constraints on existing pipeline flow direction and capacity makes it possible to assess cost reduction associated with different CO2 sources tie-in locations along existing transport pipelines. The solution was developed using the Python programming language, and small-scale demo test cases have been used to illustrate its effectiveness in four tie-in cases with single pipeline and multiple pipelines that cut across several CO2 source and sink locations. The method has also been applied to the evaluation of a proposed CarbonSAFEII project and the results used to assess optimal pipeline tie-in points for expanded sequestration capacity. The developed python package is publicly available on GitHub for researchers and economic analysts to use for evaluating large-scale carbon capture, utilization, and storage (CCUS) projects, with the aim of encouraging further development and collaboration.
- North America > United States > Wyoming > Powder River Basin (0.99)
- North America > United States > Montana > Powder River Basin (0.99)
Effects of Pipe Rotation on the Performance of Fibrous Water-Based Polymeric Fluids in Horizontal Well Cleanout
Garcia, Sergio (University of Oklahoma) | Mendez, Michael (University of Oklahoma) | Ahmed, Ramadan (University of Oklahoma (Corresponding author)) | Karami, Hamidreza (University of Oklahoma) | Nasser, Mustafa (Qatar University) | Hussein, Ibnelwaleed A. (Qatar University)
Summary The deposition of rock cuttings is a problem commonly faced during drilling, completion, and intervention operations. Using polymer-based fluids is a common technique to improve horizontal downhole cleaning. However, these fluids cannot always guarantee an efficient wellbore cleanout. One way to enhance cleanout efficiency is by rotating the drillpipe to mitigate the settling of solids and facilitate their removal. However, drillstring rotation often increases equivalent circulating density (ECD). Therefore, in this study, we explore how the impact of rotation on hole cleaning can be synergized by using fibrous water-based polymeric fluids to perform cleanout at reduced rotational speeds with limited effect on ECD. The flow loop used for this study consists of a 48-ft long eccentric annular (5×2.375 in.) test section. Each experiment began by forming a stationary bed of natural sand (an average diameter of 1.2 mm) in the test section. High-viscosity and low-viscosity polymer-based suspensions with and without fibers were used. The drillpipe rotation speed was varied from 0 to 150 rev/min. In each experiment, the flow rate was increased from 35 to 195 gal/min stepwise. The bed perimeter was measured at equilibrium condition for every test flow rate until a complete bed cleanout was achieved. In addition, the friction pressure loss was measured. Rotational viscometers were also used to measure fluid rheology before and after each test. Fiber particles improve the carrying capacity of the fluid by reducing solid settling and minimizing the redeposition of particles. The results demonstrate the effectiveness of fiber in synergizing pipe rotation effects on hole cleanout performance in horizontal wellbores. Fiber’s impact is more pronounced when used with low-viscosity fluid. The cleanout performance of the low-viscosity fluid is amplified significantly with rotation, almost entirely cleaning the bed at 75 gal/min and a rotational speed of 50 rev/min, compared with more than 195 gal/min without rotation. Even more improvement could be achieved by adding a small amount of fiber (0.04wt%). In addition, the fiber improved the cleanout performance of the high-viscosity fluid. The enhancement, however, was not as noticeable as with the low-viscosity fluid. In general, rotation combined with low-viscosity fibrous fluid exhibits the best cleaning performance. This is because rotating the pipe resuspends the settled solids, which are then easily carried by fibrous fluid that has high solids carrying capacity.
- Europe (1.00)
- Asia (1.00)
- North America > United States > Texas (0.68)
- North America > United States > Oklahoma (0.47)
- Research Report > New Finding (1.00)
- Research Report > Experimental Study (0.88)
- Well Drilling > Drilling Operations > Directional drilling (1.00)
- Well Drilling > Drilling Fluids and Materials > Drilling fluid management & disposal (1.00)
- Well Completion > Completion Installation and Operations (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production logging (1.00)
Multidimensional Data-driven Characterization of Deepwater Turbidite Deposits, Leonardian Bone Spring Formation, Delaware Basin, Southeast New Mexico and West Texas
Zhai, Rui (University of Oklahoma) | Pranter, Matthew J. (University of Oklahoma) | Pigott, John D. (University of Oklahoma)
The lower Permian Bone Spring Formation in the Delaware Basin of west Texas and southeast New Mexico comprises a succession of carbonate and siliciclastic deposits, which are the sedimentary record of extensive deepwater carbonate and sediment gravity flows on the slope and basin floor setting. The siliciclastic-dominated members are associated with prolific hydrocarbon reservoirs. This study delineates the stratigraphic architecture of the Bone Spring Formation and characterizes the deposition system of siltstone-rich turbidite deposits and their heterogeneity in the northwest of the Delaware Basin. 530 km2 of 3-D seismic data and borehole logs for 60 wells are used to are interpreted to delineate the Bone Spring sequence stratigraphy. The Bone Spring Formation is divided into six stratigraphic units. During the regressive systems tracts and lowstand systems tracts, multiple point sources from the northwestern shelf formed extensive and thick (∼110 m) turbidite deposits of the second Bone Spring siltstone unit that accumulated on the low-gradient slope and basin floor. Multiple 3-D seismic geometric attributes and spectral decomposition present the turbidite channels extend over 20 km, with lateral dimension ranges from 10 m to 120 m. Distributary channel complexes grade into sheet-sandstone lobes that are more than 100 m thick on the basin floor. Four types of lithology are identified within the siltstone unit, including siltstone, limestone, dolomized limestone, and mudstone. Considering different lithological stacking patterns exhibited on well logs, distribution is predicted using Bayesian lithology inversion. This study has far-reaching implications not only for optimizing exploitation in the immediate study area but also for sedimentary pattern recognition within the Bone Spring Formation across the Delaware Basin.
- North America > United States > Texas (1.00)
- North America > United States > New Mexico (1.00)
- Geology > Sedimentary Geology > Depositional Environment > Marine Environment > Deep Water Marine Environment (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock (1.00)
- Geology > Geological Subdiscipline > Stratigraphy (1.00)
- Geophysics > Seismic Surveying > Seismic Processing (0.90)
- Geophysics > Seismic Surveying > Surface Seismic Acquisition (0.76)
- North America > United States > Texas > Permian Basin > Delaware Basin > Wolfcamp Shale Formation (0.99)
- North America > United States > Texas > Permian Basin > Delaware Basin > Bone Springs Formation (0.99)
- North America > United States > New Mexico > Permian Basin > Delaware Basin > Wolfcamp Shale Formation (0.99)
- (25 more...)
Integrated subsurface characterization and evaluation of CO2 storage potential of the Illinois Basin Decatur Project (IBDP)
Shodunke, Ganiyat (University of Oklahoma) | Henriques, Cassian (Pennsylvania State University) | Al Maqbali, Qais (University of Oklahoma) | Madariaga, Maria P. (The University of Texas at Austin) | Alo, Olawale (University of Oklahoma) | Buckmire, Khalil (Pennsylvania State University) | Ferreira, Isabella (Pennsylvania State University)
CO2 sequestration is crucial to achieving net-zero CO2 emissions by 2050. For successful permanent storage entailing a century-long operational period, a combination of detailed subsurface characterization, static and dynamic reservoir modeling, economic feasibility analysis, and risk assessment is considered. The Illinois Basin Decatur Project (IBDP) and subsequent Industrial Carbon Capture and Storage (ICCS) have each successfully stored approximately one million tonnes of CO2 in the Mt. Simon Sandstone saline aquifer. Here, we re-evaluate the IBDP for CO2 sequestration potential and the possibility of further increasing the amount of CO2 stored to meet future demands. The dynamic reservoir model predicts the behavior of the CO2 plume and its containment over time. We estimate a storage capacity of 11 million tonnes of CO2 using a closed-boundary system. This was after a period of injection for 20 years and 100 years of monitoring. Economically, the project demonstrates favorable prospects, with an estimated cost of US$33 per tonne of CO2, considering carbon tax credits and existing infrastructure. The low-risk nature of the project and its potential for high-volume storage make it a promising candidate for meeting future CO2 reduction goals.
- Government > Regional Government > North America Government > United States Government (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- North America > United States > Kentucky > Illinois Basin (0.99)
- North America > United States > Indiana > Illinois Basin (0.99)
- North America > United States > Illinois > Illinois Basin (0.99)
- Reservoir Description and Dynamics > Storage Reservoir Engineering > CO2 capture and sequestration (1.00)
- Health, Safety, Environment & Sustainability > Sustainability/Social Responsibility > Sustainable development (1.00)
- Health, Safety, Environment & Sustainability > Environment > Climate change (1.00)
Decomposing and recovering airborne radiometric data through principal component analysis applied on flight-line data: An alternative to reduce noise
da Silva, Adolfo Barbosa (CPRM-Geological Survey of Brazil) | Pires de Lima, Rafael (CPRM-Geological Survey of Brazil, University of Colorado Boulder) | La Marca, Karelia (University of Oklahoma)
Abstract Airborne gamma-ray spectrometry (AGRS) data provide valuable information about the distribution of radiometric elements on earth’s surface. However, the presence of noise can hinder the interpretation or the identification of subtle variations of radioelement concentrations that can be economically attractive. Previous research has demonstrated that techniques based on matrix factorization, such as noise-adjusted singular value decomposition (NASDV) and minima noise fraction (MNF), can reduce noise when applied to AGRS raw spectra. Nevertheless, the raw spectra often are unavailable for end users, limiting the widespread adoption of such techniques. In this context, we use principal component analysis with the flight-line data before interpolating the data onto a regular grid as a means to reduce noise when the raw spectra are no longer available. We use our approach on two AGRS data sets located in Brazil and one in the United States. For Brazil’s AGRS data, results indicate that noise can be attenuated through eigendecomposition projection and recovery of the radiometric variables. Furthermore, our technique can highlight some geologic features dependent on the number of eigenvectors used to reconstruct the database. For the U.S. AGRS data set previously filtered with NASDV, our methodology seems to produce only marginal improvement. Therefore, our methodology might be particularly successful for AGRS data whose acquisitions were conducted before NASDV and MNF were proposed as radiometric data processing techniques.
- South America > Brazil (1.00)
- North America > United States > Colorado (0.28)
- Geology > Mineral (1.00)
- Geology > Geological Subdiscipline (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.46)
- Geophysics > Electromagnetic Surveying (0.70)
- Geophysics > Borehole Geophysics (0.49)
- Geophysics > Radioactivity Surveying > Radioactivity Acquisition (0.46)
- Geophysics > Magnetic Surveying (0.46)
- Materials > Metals & Mining (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- Government (0.68)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (0.96)
- Health, Safety, Environment & Sustainability > Environment > Naturally occurring radioactive materials (0.68)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (0.68)