The SPE has split the former "Management & Information" technical discipline into two new technical discplines:
- Management
- Data Science & Engineering Analytics
Filters
Feature
SPE Disciplines
Geologic Time
Journal
Conference
- 14th ISRM Congress (1)
- 14th World Petroleum Congress (1)
- CORROSION 2000 (1)
- Developments in Diving Technology: Proceedings of an international conference, (Divetech '84) (1)
- Offshore Mediterranean Conference and Exhibition (1)
- Offshore Technology Conference (3)
- SPE Annual Technical Conference and Exhibition (1)
- SPE Bergen One Day Seminar (5)
- SPE European Formation Damage Conference (1)
- The Fifth International Offshore and Polar Engineering Conference (1)
Author
- Aartun, D. (1)
- Adams, Waleed (1)
- Bayne, J.M. (1)
- Bjørkevoll, Knut S. (1)
- Carpenter, Chris (10)
- Chang, F.F. (2)
- Cruickshank, J. (1)
- Denney, Dennis (1)
- Dupuis, C.. (1)
- Eiane, Tor (1)
- Fjelde, Kjell Kåre (1)
- Freudenreich, M. (1)
- Fristad, Paal (1)
- Gallachóir, B.P.Ó (1)
- Gravdal, Jan Einar (1)
- Haugland, H. (1)
- Homstvedt, G. (1)
- Hove, F. (1)
- Huelvan, Yvon (1)
- Hussain, Sajjad (1)
- Høien, A. H. (1)
- Kageson-Loe, N.M. (2)
- Kuhlmann, H. (1)
- Løvaas, S. (1)
- Mathisen, A.M. (2)
- Mendoza-Barrón, V.. (1)
- Moeinikia, Fatemeh (1)
- Moon, Ted (1)
- Narum, H. (1)
- Nilsen, B. (1)
- Olsen, G.I. (1)
- Olsson, R. (1)
- Otteren, A. (1)
- Rodeman, Lance (1)
- Saasen, Arild (1)
- Sagatun, S.I. (1)
- Sarmento, A.J.N.A. (1)
- Saunders, E. (1)
- Siahaan, Hardy (1)
- Stamp, Nick (1)
- Svanes, G.S. (2)
- Thern, Holger F. (1)
- Thomas, G.P. (1)
- Thorsen, Arve Konrad (1)
- Tjelta, T.I. (1)
- Van Loo, Joseph (1)
- Vrålstad, Torbjørn (1)
- Waalmann, J.G. (1)
- Walton, I.C. (2)
- Williams, Stephen M. (1)
- Wilson, Adam (3)
Concept Tag
- algorithm (2)
- annular pressure drilling (2)
- Artificial Intelligence (9)
- artificial lift system (2)
- assembly (5)
- Baker Hughes (2)
- Berger (2)
- berle (2)
- cabr 2 (2)
- cement (2)
- choke (2)
- coefficient (2)
- Completion Installation and Operations (2)
- Completion Monitoring Systems/Intelligent Wells (2)
- complex reservoir (2)
- complexity (2)
- core barrel (2)
- core sample (2)
- coreall (2)
- correlation (2)
- debris (2)
- diameter (2)
- Directional Drilling (4)
- drilling data acquisition (2)
- Drilling Equipment (2)
- drilling fluid chemistry (3)
- drilling fluid formulation (3)
- drilling fluid management & disposal (2)
- drilling fluid property (3)
- drilling fluid selection and formulation (3)
- drilling fluids and materials (4)
- drilling measurement (2)
- drilling operation (10)
- Engineer (2)
- enhanced recovery (2)
- ensemble (2)
- equation (2)
- estimation (2)
- expansion (2)
- experiment (2)
- filter cake (2)
- flow rate (3)
- Fluid Dynamics (2)
- fluid loss (2)
- fluid loss control (3)
- formation damage (2)
- fraction (2)
- hydraulic jet pump (2)
- hydrocarbon (2)
- information (5)
- innovation (3)
- installation (5)
- investigation (2)
- junction (2)
- kill fluid (2)
- liner (2)
- logging while drilling (2)
- LWD (2)
- machine learning (4)
- managed pressure drilling (2)
- module (3)
- multiphase pump (2)
- North Sea (2)
- Norway (5)
- offshore coring (2)
- operation (5)
- operator (4)
- optimization problem (2)
- overbalance (2)
- particle (2)
- perforating (2)
- perforation (2)
- perforation tunnel (2)
- permeability (4)
- pipe (2)
- platform (2)
- pore pressure (2)
- production control (3)
- production logging (3)
- production monitoring (3)
- productivity (2)
- propagation (2)
- renewable energy (2)
- reservoir (5)
- Reservoir Characterization (4)
- Reservoir Surveillance (3)
- riser (2)
- sensor (2)
- sequence (2)
- social responsibility (3)
- specification (2)
- subsea system (4)
- sustainability (3)
- sustainable development (3)
- transducer (2)
- University (2)
- Upstream Oil & Gas (39)
- valve (2)
- water management (3)
- waterflooding (2)
Genre
Geophysics
Industry
Oilfield Places
Technology
File Type
Site News
Goto
No map tile layer
| Layer | Fill | Outline |
|---|
Map layers
| Theme | Visible | Selectable | Appearance | Zoom Range (now: 0) |
|---|
| Fill | Stroke |
|---|---|
Collaborating Authors
Vestland
Dean Oliver is a principal researcher at the Uni Centre for Integrated Petroleum Research in Bergen, Norway. He was previously director of the Mewbourne School of Petroleum and Geological Engineering at the University of Oklahoma and has also been a faculty member in the petroleum engineering department at the University of Tulsa. Before joining academia, Oliver worked for 17 years in the petroleum industry (for Chevron). He has written more than 70 peer-reviewed journal papers and is the coauthor of a book on history matching. Oliver was executive editor of SPE Journal from 2005 to 2009 and is currently editor-in-chief for SPE’s peer-reviewed journals. He received the 2004 SPE Reservoir Description and Dynamics award, the 2008 SPE Distinguished Member Award, and the 2010 SPE Distinguished Service Award.
Country:
- North America > United States > Oklahoma (0.32)
- Europe > Norway > Vestland > Bergen (0.32)
Zongchang Yang is a senior data scientist for Turbulent Flux. He has previously conducted postdoctoral research at CERN, Switzerland, and the University of Bergen, Norway. Yang has more than 10 years of experience analyzing large data sets using advanced statistical techniques. Since 2018, he has led the data science unit within Turbulent Flux, building machine learning models to compliment the physics-based approach to our multiphase transient flow simulations. Yang holds a PhD degree in particle physics from Peking University in China.
SPE Disciplines:
Technology:
One of the chief goals of an offshore exploration and appraisal program involves taking core samples of the targeted reservoir. Embedded within that reservoir rock are valuable data sets that include direct measurements of relative permeability, wettability, and fluid properties. This information is considered essential for fine-tuning reservoir flow models and guiding exploration geologists to subsequent drilling prospects. But despite the importance of subsea coring, it is not always a straightforward operation because conventional methods rely on surface measurements to determine where to begin taking the samples--which means a core might be collected from the wrong location. CoreAll, a technology startup based near Bergen, Norway, has developed a new coring assembly to solve for this issue.
Oilfield Places:
- Europe > Norway > North Sea > Central North Sea > Utsira High > PL 501 > Block 16/5 > Johan Sverdrup Field > Zechstein Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > Utsira High > PL 501 > Block 16/5 > Johan Sverdrup Field > Viking Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > Utsira High > PL 501 > Block 16/5 > Johan Sverdrup Field > Vestland Group (0.99)
- (27 more...)
SPE Disciplines:
One of the chief goals of an offshore exploration and appraisal program involves taking core samples of the targeted reservoir. Embedded within that reservoir rock are valuable data sets that include direct measurements of relative permeability, wettability, and fluid properties. This information is considered essential for fine-tuning reservoir flow models and guiding exploration geologists to subsequent drilling prospects. But despite the importance of subsea coring, it is not always a straightforward operation because conventional methods rely on surface measurements to determine where to begin taking the samples--which means a core might be collected from the wrong location. CoreAll, a technology startup based near Bergen, Norway, has developed a new coring assembly to solve for this issue.
Oilfield Places:
- Europe > Norway > North Sea > Central North Sea > Utsira High > PL 501 > Block 16/5 > Johan Sverdrup Field > Zechstein Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > Utsira High > PL 501 > Block 16/5 > Johan Sverdrup Field > Viking Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > Utsira High > PL 501 > Block 16/5 > Johan Sverdrup Field > Vestland Group (0.99)
- (27 more...)
SPE Disciplines:
Wood has agreed to a deal with Equinor to perform modifications to two platforms in the Norwegian North Sea (Snorre A and Gullfaks A) that are set to receive electric power from floating wind turbines. As part of the 3-year contract, estimated to be worth more than £20 million, Wood will provide the topside modifications necessary for the Snorre A and Gullfaks A platforms to integrate the Hywind floating wind park with existing systems powering the facilities. The scope of work also includes equipment installation on the floating wind turbines and upgrades to the onshore control room in Bergen, Norway, that will remotely operate the wind farm. "The Snorre A and Gullfaks A facilities will be the first oil and gas platforms to be powered by a floating offshore wind farm. We are proud to support Equinor on what is a flagship project for the North Sea's energy transition journey. Wood is fully committed to applying our experience gained from decades of working in the region's oil and gas industry to reduce the carbon intensity of offshore operations by modifying existing infrastructure," Dave Stewart, CEO of Wood's asset solutions business in Europe, Africa, Asia, and Australia, said in a statement.
Oilfield Places:
- Europe > United Kingdom > North Sea > Central North Sea > Central Graben > Block 22/18 > MonArb Field > Wood Field (0.98)
- Europe > Norway > North Sea > Northern North Sea > East Shetland Basin > PL 375 > Block 34/7 > Snorre Field > Statfjord Group (0.94)
- Europe > Norway > North Sea > Northern North Sea > East Shetland Basin > PL 375 > Block 34/7 > Snorre Field > Lunde Formation (0.94)
- (15 more...)
SPE Disciplines:
This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 195601, “Systematic Selection of Drill-In and Completion Fluids for Development of the Dvalin High-Temperature Gas Field,” by Oliver Czuprat, SPE, Bjorn Olav Dahle, and Ulf Dehmel, DEA, et al., prepared for the 2019 SPE Norway One Day Seminar, Bergen, Norway, 14 May. The paper has not been peer reviewed. For the development of the Dvalin high-pressure/high-temperature (HP/HT) gas field in the Norwegian Sea, a completion scheme using standalone screens is planned. To secure maximum cleanup and productivity, even after long-term suspension, comprehensive laboratory testing was performed to evaluate specific properties from drilling and completion fluids at downhole conditions. The complete paper details the results of all test phases. With the test methodology, several proposed mud-system candidates were disqualified at an early stage, thus saving time and cost for subsequent formation-damage testing and complementary analytics. Introduction Formation damage is believed to have caused difficulties in modular-dynamic-tester sampling in the high-permeability zone of Dvalin West. The damage mechanism has been investigated in a study that revealed both fluid systems to have good fluid-loss-control properties, as observed in drilling operations. However, both systems showed damage potential. The extent of damage is more pronounced in Dvalin West. The field-development plan calls for four producing wells to be drilled (two in each structure). Prevention or, realistically speaking, minimization of impaired production as a result of formation damage has been identified as a priority. A completion scheme with standalone screens requires additional specific properties from drilling and completion fluids at downhole conditions. The production facilities will be commissioned after the completion of drilling, and there are no provisions for handling cleanup flow through pipeline and topsides. Thus, all wells will be cleaned up to a temporary test plant on-board the drilling rig. The lag time between drilling the wells and cleaning them will be 2–3 weeks, underlining the necessity of a drilling mud that is stable over a significant period of time while retaining inherent mobility. Six reservoir drill-in fluids (water- and oil-based) were proposed by different vendors, and samples thereof and the corresponding screen fluids (if available) were provided. The systematic test program consisted of a sequence of four test phases, where only successful fluids went to the next phase (Fig. 1). A description and application of equipment and processes is provided in the complete paper. Data and Results Phase I. The first test phase comprised a set of simple screening laboratory tests. The best-in-class drilling and completion fluids from the study were then tested with regard to formation damage (return permeability tests). Tested qualities included density, particle size distribution, rheology, mobility, settling, emulsion stability, HP/HT filtration and rheology, compatibility, and production-screen testing. As detailed in the complete paper, many of the fluids demonstrated acceptable or good performance in many of these aspects; nevertheless, on the basis of observations during Phase I, two out of six fluid systems could already be excluded as unstable for the application (Drilling Fluids 1 and 6).
batch, compatibility, Completion Fluid, complex reservoir, drilling fluid 2, drilling fluid 3, drilling fluid 4, drilling fluid chemistry, drilling fluid formulation, drilling fluid management & disposal, drilling fluid property, drilling fluid selection and formulation, drilling fluids and materials, flow in porous media, fluid 4, Fluid Dynamics, fluid loss control, Gasfield Development, high pressure high temperature, HPHT, production-screen testing, return permeability, rheology, stability, test methodology optimize selection, test phase, Upstream Oil & Gas
Oilfield Places:
- Europe > Norway > Norwegian Sea > Halten Terrace > PL 435 > Block 6507/7 > Dvalin Field > Ile Formation (0.99)
- Europe > Norway > Norwegian Sea > Halten Terrace > PL 435 > Block 6507/7 > Dvalin Field > Garn Formation (0.99)
- Europe > Norway > Norwegian Sea > Halten Terrace > PL 435 > Block 6507/7 > Dvalin Field > Fangst Formation (0.99)
SPE Disciplines:
Abstract The swelling of gouge material is an important factor to consider when excavating tunnels through weakness zones. The swelling that occurs in a weakness zone is mainly caused by the expansion of clay minerals belonging to the smectite group. The objective of this paper is to examine the behaviour of the swelling fraction of gouge material with different initial water contents. Oedometer testing of the fraction < 20 μm was used to identify potential swelling pressure and the behaviour of swelling gouges. A test setup with different degrees of initial water content was applied for two different materials originating from a road tunnel at highway E18 in Vestfold and highway E39 in Bergen, Norway, respectively. Seven tests were performed on the first material and eight were performed on the second. For each material, two tests were conducted on dry material and the rest of the material with various water contents. In these experiments, a correlation was found between the sample height and the swelling pressure. It was also found that the water content had a significant influence on pre-compacting, with higher water content giving lower samples heights. Based on this, it may be possible to identify a water content when all the intracrystalline swelling/hydration has completed. This is assumed to be important in assessing the test results for the in-situ material as one may consider to what degree swelling has already occurred based on the in-situ water content. 1 Introduction and background When excavating tunnels in hard rock conditions, the crossing of weakness zones, which often contain swelling minerals, poses a significant challenge, and knowledge about the swelling potential is important for decisions regarding excavation and rock support. The swelling that occurs in weakness zone gouge material is an expansion caused mainly by clay minerals in the smectite-group (Brekke & Selmer-Olsen, 1965, Selmer-Olsen, 1985).
apparatus deformation, consolidation, correlation, deformation, drilling fluid management & disposal, drilling fluids and materials, expansion, Figure, figure 11, fraction, hydration, intracrystalline, MPa, post-swelling consolidation, preparation, quasicrystal, selmer-olsen, setup, staten vegvesen, table 3, testing, Upstream Oil & Gas
SPE Disciplines: Well Drilling > Drilling Fluids and Materials > Drilling fluid management & disposal (1.00)
This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 195615, “A Data-Driven Management Strategy To Reduce the Environmental Impact of Upstream Production Plants,” by Luca Cadei, SPE, Danilo Loffreno, Giuseppe Camarda, Marco Montini, Gianmarco Rossi, SPE, Piero Fier, Davide Lupica, Andrea Corneo, Lornzo Lancia, Diletta Milana, Marco Carrettoni, and Elisabetta Purlalli, Eni, and Francesco Carduccu and Gustavo Sophia, The Boston Consulting Group GAMMA, prepared for the 2019 SPE Norway One Day Seminar, Bergen, Norway, 14 May. The paper has not been peer reviewed. This paper highlights the results of a test campaign for a tool designed to predict the short-term trends of energy-efficiency indices and optimal management of a production plant. The•developed tool represents a step toward digital transformation of production plants through the integration of data analytics and machine-learning methodologies with•expert domain•knowledge. Introduction The tool, called the Energy-Efficiency Predicting and Optimizing Digital-System Tool, was developed as an in-house product for the purpose of helping operators select a series of corrective measures and optimized management actions for an oil and gas production plant. The entire procedure relies on the definition of several key performance indicators (KPIs). The KPIs represent a combination of process parameters useful for understanding, summarizing, and comparing the performance of entire plants or individual equipment units. Comparing the actual KPI vs. past behavior, or a target value, allows operators to understand how the plant and equipment are performing and whether their energy performance can be improved. Specifically, the tool consists of a machine-learning-based forecasting model and a series of aggregated analytics. The machine-learning model, based on a gradient-boosting regression (GBR) algorithm, predicts the global KPI known as the Stationary-Combustion CO2 Emission Index, allowing operators to estimate future energy efficiency. Along with the forecast, the tool shows aggregated statistics for KPIs of individual equipment units. Materials and Methods Case Study. The producing field considered within the current project is on-shore southern Europe. The central processing facility includes five production lines (trains) implemented in separated phases to treat the multiphase flow from•the wells. The multiphase flow comes from 27 producers and consists of three main phases: gas, oil, and water. The composition of the oil differs according to the formation in which a well has been drilled. This oil has different characteristics from other concessions in Europe, including H2S ranging from 0.5 to 1.5% mol, and CO2 ranging from 5 to 30% mol. The final scope of the plant is to produce stabilized oil, treated gas, and liquid sulfur, which are then commercialized with the following strict specifications: The oil is sent by means of a 100-km-long pipeline to a refinery. The gas is sold to the national gas grid, managed by a third party. The liquid sulfur, with a purity of 99.9%, is sold to the pharmaceutical and explosives industries. These energy conversions generate CO2 emissions from the stationary combustion of fuel gas. In accordance with the Paris Agreement charge to limit the rise in global temperature to below 2°C compared with preindustrial levels, reducing CO2 emissions from this asset is critical. Real-time monitoring and prediction can help meet•this goal.
air emission, algorithm, Artificial Intelligence, climate change, co 2, consumption, data-driven management strategy, Engineer, equipment unit, feature generation, information, machine learning, operator, production plant, reboiler, recommendation, reduce environmental effect, regression, social responsibility, specification, sustainability, sustainable development, tco 2, total co 2, Upstream Oil & Gas
SPE Disciplines:
This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 191305, “Correlation-Based Adaptive Localization for Ensemble-Based History Matching Applied to the Norne Field Case Study,” by Xiaodong Luo, SPE, Rolf Lorentzen, SPE, Randi Valestrand, SPE, and Geir Evensen, SPE, International Research Institute of Stavanger, prepared for the 2018 SPE Norway One Day Seminar, Bergen, Norway, 18 April. The paper has been peer reviewed and published in the October 2018 SPE Journal. Ensemble-based methods are considered to be state-of-the-art history-matching algorithms. However, in practice, they often suffer from ensemble collapse, a phenomenon that deteriorates history-matching performance. An ensemble history-matching algorithm is equipped customarily with a localization scheme to prevent ensemble collapse. To enhance the applicability of localization to various history-matching problems, the authors adopt an adaptive localization scheme that exploits the correlations between model variables and observations. Introduction In the current work, the authors focus on adopting an efficient adaptive localization scheme, previously established in the literature, for the full Norne Field case study. The adaptive localization scheme exploits the information of sample correlation coefficients between an ensemble of model variables and the corresponding realizations of simulated observations. The adaptive localization scheme uses a data-selection procedure; however, instead of physical distances between the locations of model variables and observations being used for data selection, the magnitudes of the sample correlation coefficients are used for data selection through a hard-thresholding strategy (i.e., keep or kill). To conduct data selection in the adaptive localization scheme, one specifies a positive correlation-threshold value. For a given observation, if the magnitude of the sample correlation coefficient between a model variable and the simulated observation is greater than the threshold value, then the observation will be used to update that model variable. Otherwise, one discards the observation in the update of that model variable. This described data-selection procedure essentially means that a given model variable is updated using only the observations that have significant correlations with the model variable. The rationale behind this hard-thresholding strategy is the interpretation of the magnitude of the correlation coefficient as a measure to detect the causal relation between a model variable and an observation, and the effect is the suppression of spurious correlations caused by a finite sample size. Correlation-based localization can overcome or alleviate the issues arising in distance-based localization, such as the use of nonlocal and time-lapse observations, the need of physical locations for model variables and measurements, and the different degrees of correlations or sensitivities of model variables to observations. Because data selection depends on the magnitudes of sample correlation coefficients between model variables and the corresponding simulated observations, the measurements need not have associated physical locations. Model variables are thus selected by using those observations that exhibit strong-enough correlations, regardless of the physical distances between observations and model variables. As a result, correlation-based localization can be used to localize nonlocal observations. The changes of correlations caused by the effect of time-lapse observations or different types of model variables will be taken into account automatically in correlation-based localization, and this makes the proposed localization scheme more-adaptive and more-flexible in various situations.
Artificial Intelligence, case study, correlation coefficient, correlation-based localization, correlation-based localization scheme, data mismatch, distance-based localization, ensemble, ensemble collapse, ensemble-based history matching, history matching, localization, localization scheme, machine learning, model variable, production data, reservoir simulation, sample correlation coefficient, threshold value, Upstream Oil & Gas
Country:
Oilfield Places:
- Europe > Norway > Norwegian Sea > Halten Terrace > PL 128 > Block 6608/10 > Norne Field > Tofte Formation (0.99)
- Europe > Norway > Norwegian Sea > Halten Terrace > PL 128 > Block 6608/10 > Norne Field > Not Formation (0.99)
- Europe > Norway > Norwegian Sea > Halten Terrace > PL 128 > Block 6608/10 > Norne Field > Ile Formation (0.99)
- (3 more...)
SPE Disciplines:
Technology:
This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 191337, “An Interactive Decision Support System for Geosteering Operations,” by Sergey Alyaev, IRIS; Reidar Brumer Bratvold, SPE, University of Stavanger; and Xiaodong Luo, SPE, Erich Suter, SPE, and Erlend H. Vefring, SPE, IRIS, prepared for the 2018 SPE Norway One Day Seminar, Bergen, Norway, 18 April. The paper has not been peer reviewed. To place a well in the best possible reservoir zone, operators use geosteering to support real-time well-trajectory adjustments. Geosteering refers to the process of making directional well adjustments on the basis of real-time information acquired while drilling. This work presents a systematic geosteering work flow that automatically integrates a priori information and real-time measurements to update geomodels with uncertainties and uses the latest model predictions in a decision-support system (DSS). The DSS supports geosteering decisions by evaluating production potential against drilling and completion risks. Introduction This paper presents a consistent, systematic, and transparent work flow for geosteering. The starting point is a priori information, for example a probabilistic geomodel representing a geological interpretation based on surface seismic and logs from offset wells, including relevant interpretation uncertainties. Multiple geomodel realizations of the possible geological scenarios span the space of interpretation uncertainties. The real-time measurements obtained while drilling are continually integrated by updating the realizations automatically using an ensemble-based filtering method. The real-time update of the realizations leads to a reduction in interpretation uncertainty, providing up-to-date predictions of the geology ahead of the bit consistently. The update work flow is linked to a DSS. The DSS applies the probabilistic up-to-date geomodel to support geosteering decisions under uncertainty by evaluating the chosen value function of the well. The value function commonly includes multiple objectives, including production potential, costs for drilling and completion, and risks associated with the operation. The DSS presented here is optimized specifically for use with ensemble-based update work flows that are increasingly popular in the oil industry. The focus of this paper is the DSS. The DSS suggests steering correction or stopping, optimizing well trajectories over the ensemble of up-to-date geomodel realizations. A graphical user interface (GUI) enables geosteering experts to control the input to the DSS by means of interactive selection and adjustment of value functions and constraints (e.g., dogleg severity). The adjustments are applied in a matter of seconds using advanced dynamic programming algorithms that yield consistently updated decisions. The proposed steering decision is communicated through the GUI, which contains a visual representation of the current uncertainty and trajectory possibilities that give the best value for each realization.
adjustment, algorithm, Artificial Intelligence, decision point, drilling operation, DSS, ensemble, geomodel realization, geosteering, information, multiple objective, objective, optimization problem, real time system, realization, Reservoir Characterization, Reservoir Navigation, trajectory, update loop, Upstream Oil & Gas, work flow integrate real-time measurement
SPE Disciplines:
Technology:
Save or update focal point
Save a search / focal point to access it again.
Thank you!