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Strong storms can trigger steep, breaking waves that slam into platform and wind turbines with tremendous force. Scientists at the Norwegian University of Science and Technology (NTNU) and SINTEF are studying the behavior of offshore structures subjected to these kinds of waves. Their goal is to increase safety at sea. The force of waves slamming into offshore rigs, wind turbine pillars, ships or other offshore structures can do an enormous amount of damage. One of the fundamental--and unresolved--problems with designing these kinds of large structures is being able to predict exactly how they will react to extreme stresses.
Flow assurance in subsea oil and gas fields often presents significant challenges. Every field has its own combination of difficulties, and no universal process or system can be used to mitigate these. Detailed knowledge across a broad range of competencies, therefore, is required to find solutions that can minimize the risk of not getting the hydrocarbons safely to the process facilities. Many subsea fields that are being developed today are long tiebacks, taking advantage of existing offshore infrastructure or producing directly to shore. These developments must deal with the long-distance transport of hydrocarbons in deep cold water, commonly increasing the risk of hydrate formation and wax deposition, for example.
Are you trying to stay up to date about developments aimed at energy-transition efforts in our industry? Green Energy Billionaires While the oil and gas industry may not yet be profiting from the energy transition, a number of individuals are. According to Forbes, as of April, there were 34 billionaires throughout the world whose fortunes stem from clean energy. Here are the top 10. Clients include BMW, Volkswagen, and Geely.
An exploration well drilled by Wintershall on its Dvalin North prospect in the Norwegian Sea has encountered a significant gas reservoir. The discovery at Dvalin North is estimated to hold to hold 33–70 million BOE and is just 12 km north of the company's operated Dvalin field and 65 km north of the operated Maria field. The well also encountered hydrocarbons in two shallower secondary targets, with a combined resource estimate of 38–87 million BOE, making the potential for the field in excess of 150 million BOE. The well, drilled by the Deepsea Aberdeen rig, encountered gas, condensate, and oil columns of 33 m and 114 m in the Cretaceous Lysing and Lange formations, respectively. In the primary target in the Garn Formation, the well found a gas column of 85 m.
Norway's Ministry of Petroleum and Energy has awarded four production licenses in its 25th licensing round, part of the numbered rounds that focus on unexplored frontier areas. One license is in the Norwegian Sea, and three are in the Barents Sea. The companies involved with the awards include Norske Shell, Equinor, Idemitsu Petroleum Norway, INEOS E&P Norway, Lundin, OMV (Norway), and Vår Energi. The seven participating companies represents a drop from the 11 companies that sought acreage in the 24th round in 3 years ago and the 26 operators that participated in the 23rd round held in 2016. Norway's Equinor was offered stakes in a pair of permits and operate them both.
Reporting on the announcement of Norway's first offshore wind tender the week of 7 June, Reuters said, "Oslo will present details of the tender as part of a whitepaper on the energy sector." But the release of the whitepaper on 11 June put to rest any hope or concern that the Norwegian government was about to put all--or even most--of its "energy eggs" in the renewables "basket." "The government wants Norwegian energy resources to form the basis for more jobs and prosperity in society. Thus, the whitepaper has been titled Putting Energy to Work," said a press release. According to the government statement, Norway's position as an energy nation will be developed further through new initiatives encompassing hydrogen, offshore wind, strengthening the power grid, and a low-emissions oil and gas sector.
Equinor and partners Total E&P Norge AS and Vår Energi AS have struck oil and gas in a new segment belonging to the Tyrihans field in the Norwegian Sea. Exploration well 6407/1-A-3 BH in production license 073 was drilled from subsea template A at Tyrihans North. The well was drilled to a measured depth of 5332 m by semisubmersible drilling rig Transocean Norge and struck a gas column of about 43 m and an oil column of about 15 m in the Ile formation, including about 76 m of moderate to good reservoir quality sandstone. In the Tilje formation, moderate to good quality water-bearing reservoir was struck. The Tyrihans field is in the middle of the Norwegian Sea, some 25 km southeast of the Åsgard field and 220 km northwest of Trondheim.
Abstract The Cretaceous Cape Vulture prospect (Norwegian Sea, Norway) consisted of three Cretaceous sand levels: Cape Vulture Lower, Main, and Upper. The prospect was drilled in 2017, targeting seismic amplitude anomalies that represented a combination of reservoir facies and hydrocarbons. As the first well (6608/10-17S) proved hydrocarbons down to base reservoir in Cape Vulture Main and Upper, an appraisal well with two sidetracks were planned and drilled to determine the reservoir development, pressure communication and oil-water contact. A good understanding of the lateral variation within the reservoir was of importance to the technical economical evaluation of the discovery. The appraisal wells planned for a comprehensive coring and logging program. The main objectives were to reduce the uncertainty of estimated in place volumes by establishing the depth of the hydrocarbon-water contact, prove lateral pressure communication within each reservoir level, reduce the uncertainty of lateral and vertical reservoir distribution and quality, reduce the uncertainty of hydrocarbon saturation and understand the relationship between seismic amplitude anomalies and subsurface properties / fluids. The logging program included triaxial resistivity, nuclear spectroscopy, electrical images, nuclear magnetic resonance (NMR) complementing triple combo, followed by formation pressure measurements, and fluid sampling. The presence of clay minerals in varying amounts within the reservoirs depresses the resistivity measurement and leads to underestimation of the hydrocarbon saturation when using conventional Archie’s equation - a common petrophysical challenge in such conditions. The hydrocarbon saturation is an important parameter when calculating reserves and estimating whether a discovery is of commercial value. Hence, reducing the uncertainty span on hydrocarbon saturation (total and effective) and estimating the net pay thickness is critical. Using core data and advanced down-hole measurements to optimize a resistivity-based saturation model can reduce the uncertainty of the saturation estimates. Here we document the petrophysical evaluation of the data acquired, assessing heterolithic low resistivity pay with wireline log measurements combined with core data. Focus on the coring strategy, recommendations on sampling intervals for the core analysis, and key logging measurement requirements. The results show substantial improvements in the understanding of the hydrocarbon saturation, ultimately increasing in-place volume estimates. The integrated analysis, including NMR measurements, helps to delineate the fluid contacts, further reducing the uncertainty on the recoverable net pay thickness. The core data validate the independent log-based laminated sand analysis. This illustrates how an integrated approach combining core measurements, logs, and formation testing provide an accurate evaluation of low resistivity pay reservoirs, reducing the uncertainty in the technical economical evaluation.
Abstract During well planning, drillers and petrophysicists have different principle objectives. The petrophysicist’s aim is to acquire critical well data, but this can lead to increased operational risk. The driller is focused on optimizing the well design, which can result in compromised data quality. In extreme cases, the impact of well design on petrophysical data can lead to erroneous post-well results that impact the entire value-chain assessment and decision making toward field development. In this paper, we present a case study from a syn-rift, Upper Jurassic reservoir in the Norwegian Sea where well design significantly impacted reservoir characterization. Three wells (exploration, appraisal, and geopilot) are compared in order to demonstrate the impact of overbalanced drilling on well data from both logs and core. Implications for reservoir quality assessment, volume estimates, and the errors introduced into both a static geomodel and dynamic reservoir simulation are discussed. This case study highlights the importance of optimizing well design for petrophysical data collection and demonstrates the potential for value creation. Extensive data collection was initially carried out in both exploration and appraisal wells, including full sets of logging while drilling (LWD), wireline logging, fluid sampling, and extensive coring. Both wells were drilled with considerable overbalanced mud weights due to the risk of overpressured reservoirs in the region. The log data was subsequently corrected for significant mud-filtration invasion, with calibration to core measurements guiding the interpretation. Geological and reservoir models were built based on results from the two wells, and development wells were planned accordingly. A thorough investigation of core material raised suspicion that there could also be a significant adverse effect of core properties resulting from overbalanced drilling. The implications were so significant for the reservoir volume that a strategic decision was made to drill a geopilot well close to the initial exploration well, prior to field development drilling. The well was drilled six years after the initial exploration phase with considerably lower overbalance. Extensive well data, including one core, were acquired. The recovered core was crucial in order to compare the reservoir properties for comparable facies between all three wells. The results from the core demonstrate distinctly different rock quality characteristics, especially at the high end of the reservoir quality spectrum. Results of the core study confirmed the initial hypothesis that overbalanced drilling had significantly impacted the properties of the core as well as the well logs. The study concluded that the updated reservoir model properties would significantly increase the in-place volumes compared to the pre-geopilot estimate. This study shows how well design adversely affected petrophysical measurements and how errors in these data compromised geological and reservoir models, leading to a suboptimal field development plan that eroded significant value. This example provides a case study that can be used to improve the well design so that petrophysicists and drillers can both be part of the same value creation result. Future work will include further laboratory investigations on the effects of high overbalanced drilling on core and possible “root causes” for compromised core integrity.
Craddock, Paul (Schlumberger-Doll Research Center) | Srivastava, Prakhar (Schlumberger-Doll Research Center) | Datir, Harish (Schlumberger) | Rose, David (Schlumberger) | Zhou, Tong (Schlumberger) | Mosse, Laurent (Schlumberger) | Venkataramanan, Lalitha (Schlumberger)
Abstract This paper describes an innovative machine learning application, based on variational autoencoder frameworks, to quantify the concentrations and associated uncertainties of common minerals in sedimentary formations using the measurement of atomic element concentrations from geochemical spectroscopy logs as inputs. The algorithm comprises an input(s), encoder, decoder, output(s), and a novel cost function to optimize the model coefficients during training. The input to the algorithm is a set of dry-weight concentrations of atomic elements with their associated uncertainty. The first output is a set of dry-weight fractions of fourteen minerals, and the second output is a set of reconstructed dry-weight concentrations of the original elements. Both sets of outputs include estimates of uncertainty on their predictions. The encoder and decoder are multilayer feed-forward artificial neural networks (ANN), with their coefficients (weights) optimized during calibration (training). The cost function simultaneously minimizes error (the accuracy metric) and variance (the precision or robustness metric) on the mineral and reconstructed elemental outputs. Training of the weights is done using a set of several-thousand core samples with independent, high-fidelity elemental and mineral (quartz, potassium-feldspar, plagioclase-feldspar, illite, smectite, kaolinite, chlorite, mica, calcite, dolomite, ankerite, siderite, pyrite, and anhydrite) data. The algorithm provides notable advantages over existing methods to estimate formation lithology or mineralogy relying on simple linear, empirical, or nearest-neighbor functions. The ANN numerically capture the multi-dimensional and nonlinear geochemical relationship (mapping) between elements and minerals that is insufficiently described by prior methods. Training is iterative via backpropagation and samples from Gaussian distributions on each of the elemental inputs, rather than single values, for every sample at each iteration (epoch). These Gaussian distributions are chosen to specifically represent the unique statistical uncertainty of the dry-weight elements in the logging measurements. Sampling from Gaussian distributions during training reduces the potential for overfitting, provides robustness for log interpretations, and further enables a calibrated estimate of uncertainty on the mineral and reconstructed elemental outputs, all of which are lacking in prior methods. The framework of the algorithm is purposefully generalizable that it can be adapted across geochemical spectroscopy tools. The algorithm reasonably approximates a ‘global-average’ model that requires neither different calibrations nor expert parameterization or intervention for interpreting common oilfield sedimentary formations, although the framework is again purposefully generalizable so it can be optimized for local environments where desirable. The paper showcases field application of the method for estimating mineral type and abundance in oilfield formations from wellbore logging measurements.