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- Volume 29, Issue 1, 2023
Petroleum Geoscience - Volume 29, Issue 1, 2023
Volume 29, Issue 1, 2023
- Research article
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Gas permeability change with deformation and cracking of a sandstone under triaxial compression
Authors Yuan-Jian Lin, Jiang-Feng Liu, Tao Chen, Bing-Xiang Huang, Shi-Jia Ma and Hai-Bo BaiIn this study, a thermal–hydraulic–mechanical–chemical (THMC) multi-field coupling triaxial cell was used to study systematically the evolution of gas permeability and the deformation characteristics of sandstone. The effects of confining, axial and gas pressure on gas permeability characteristics were fully considered in the test. The gas permeability of sandstone decreases with increasing confining pressure. When the confining pressure is low, the variation of gas permeability is greater than that of gas permeability at high confining pressure. The gas injection pressure significantly affects the gas permeability evolution of sandstone. As the gas injection pressure increases, the gas permeability of sandstone tends to decrease. At the same confining pressure, the gas permeability of the sample during the unloading path is less than the gas permeability of the sample in the loading path. When axial pressure is applied, it has a significant influence on the permeability evolution of sandstone. When the axial pressure is less than 30 MPa, it significantly influences the permeability evolution of sandstone. At axial pressures greater than 30 MPa, the permeability decreases as the axial pressure increases. Finally, the micro-pore/fracture structure of the sample after the gas permeability test was observed using 3D X-ray CT imaging.
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Rock mechanical properties of immature, organic-rich source rocks and their relationships to rock composition and lithofacies
Mechanical properties of layered rocks are critical in ensuring wellbore integrity and predicting natural fracture occurrence for successful reservoir development, particularly in unconventional reservoirs for which fractures provide the main pathway for hydrocarbon flow. We examine rock mechanical properties of exceptionally organic-rich, immature source rocks from Jordan, and understand their relationships with rock mineral composition and lithofacies variations. Four depositional microfacies were identified: organic-rich mudstone, organic-rich wackestone, silica-rich packstone and fine-grained organic-rich wackestone. The four types exhibit various mineralogical compositions, dominated by carbonates, biogenic quartz and apatite. Leeb hardness ranges between 288 and 654, with the highest average values occurring in silica-rich packstone and organic-rich mudstone. The highest uniaxial compressive strength (derived from the intrinsic specific energy measured using an Epslog Wombat scratch device), and compressional- and shear-wave velocities were measured in organic-rich mudstones (140 MPa, 3368 m s−1 and 1702 m s−1, respectively). Porosity shows higher average values in organic-rich wackestones and fine-grained organic-rich wackestones (33–35%). Silica-rich packstone and organic-rich mudstone have brittle properties, while organic-rich wackestone and fine-grained organic-rich wackestone are ductile. High silica contents are correlated positively with brittleness. A strong hardness–brittleness correlation suggests that Leeb hardness is a useful proxy for brittleness. Our study allows a better understanding of the relationships between lithofacies, organic content and rock mechanical properties, with implications for fracking design to well completion and hydrocarbon production. Further work involving systematic sampling and a more rigorous study is still required to better understand the spatial distribution of target lithologies and their mechanical properties.
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Integrated approach to improve simulation models in a deep-water heavy oil field with 4D seismic monitoring
The geological features revealed by well production data or 4D seismic are often neglected in data assimilation or are disconnected from the geomodelling tasks through simplifications on static and dynamic data. This work provides a workflow to accurately integrate 4D seismic insights through a forward geomodelling approach and provides prior simulation models calibrated with observed dynamic data. The methodology follows four steps: (1) develop the geological model; (2) generate equiprobable geostatistical realizations based on the multiple stochastic approach; (3) apply the discretized Latin Hypercube sampling technique combined with geostatistics realizations (DLHG) method; and (4) validate the geological consistency and uncertainty quantification using the observed dynamic data. The methodology is applied to a real turbiditic reservoir, a heavy oil field in the offshore Campos Basin, Brazil. From the 4D seismic datasets, the following data were available: (1) base survey; (2) monitor-2016; and (3) monitor-2020. The interpreted 4D seismic trends were integrated in the geological model by combining the geometrical modelling technique, for observed structural features, with the objects’ modelling approach, for the observed sand channels. The geostatistical realizations were then combined with dynamic uncertainties using the DLHG method. The quantitative validation based on the Normalized Quadratic Deviation with Signal (NQDS) indicator showed that the generated prior simulation models encompass the observed production data. In addition, the match with observed 4D seismic data based on difference of the root mean square (dRMS) amplitude maps highlighted the value of adding 4D seismic information. This paper presents a successful forward modelling approach to highlight the value of 4D seismic on the calibration of simulation models prior to data assimilation.
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- Review article
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Challenges for seismic velocity modelling of rafts and impacts for pre-salt depth estimations
More LessSeismic velocity models have significant importance in subsurface studies, notably when applied in structurally challenging areas. In some parts of the Campos Basin, offshore Brazil, the pre-salt reservoir's overburden shows complex structures, mainly due to raft tectonism that positions laterally, resulting in interspersed salt domes, carbonate rafts and siliciclastic sediments. This work used an extensive well database in the Marlim Complex to analyse the raft seismic velocities and their related impacts on pre-salt reservoir models. Based on well data, in combination with detailed seismic interpretation, seven alternative velocity scenarios were proposed for the rafts. The geological motivations for each scenario are discussed with the aim of developing constrained depth models for pre-salt reservoirs. The depth forecast results could be tested by drilled wells, and the resulting models are quantitatively compared in terms of depth predictions and gross-rock volumes. The results show that the topography of the target pre-salt reservoirs can vary considerably, even in scenarios where well and geological constraints are considered. This can impact pre-salt geological characterization and field development.
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- Thematic collection: Digitally enabled geoscience workflows: unlocking the power of our data
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Predicting oil field performance using machine learning programming: a comparative case study from the UK continental shelf
Authors Ukari Osah and John HowellPredicting the performance of a subsurface oil field is a large, multivariant problem. Production is controlled and influenced by a wide array of geological and engineering parameters which overlap and interact in ways that are difficult to unravel in a manner that can be predictive. Supervised machine learning is a statistical approach which uses empirical learnings from a training dataset to create models and make predictions about future outcomes. The goal of this study is to test a number of supervised machine learning methods on a dataset of oil fields from the United Kingdom continental shelf (UKCS), in order to assess whether, (a) it is possible to predict future oil field performance and (b), which methods are the most effective. The study is based on a dataset of 60 fields with 5 controlling parameters, (gross depositional environment, average permeability, net-to-gross, gas–oil ratio and total number of wells) and 2 outcome parameters (recovery factor and maximum field rate) for each. The choice of controlling parameters was based on a PCA of a larger dataset from a wider project database. Five different machine learning algorithms were tested. These include linear regression, robust linear regression, linear kernel support vector regression, cubic kernel support vector regression and boosted trees regression. Overall, 83% of the data was used as a training dataset while 17% was used to test the predictability of the algorithms. Results were compared using R-Squared, Mean Square Error, Root Mean Square Error and Mean Absolute Error. Graphs of predicted responses v. true (actual) responses are also shown to give a visual illustration of model performance. Results of this analysis show that certain methods perform better than others, depending on the outcome variable in question (recovery factor or maximum field rate). The best method for both outcome variables was the support vector regression, where, depending on the kernel function applied, a reliable level of predictability with low error rates were achieved. This demonstrates a strong potential for statistics-based prediction models of reservoir performance.
Thematic collection: This article is part of the Digitally enabled geoscience workflows: unlocking the power of our data collection available at https://www.lyellcollection.org/topic/collections/digitally-enabled-geoscience-workflows
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- Thematic collection: Geopressure
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Jasmine: the challenges of delivering infill wells in a variably depleted HPHT field
More LessDrilling infill wells into a heavily depleted reservoir poses several challenges that can lead to increased time, cost and risk. Data acquisition, including gathering formation pressure data, can be severely compromised, complicating real-time decisions and pore pressure interpretation. Fracture gradients, usually constrained by data acquired outside the reservoir, need to be estimated using a different approach through a depleted reservoir. The Jasmine high-pressure high-temperature (HPHT) field in the UK Central North Sea can be used to illustrate some of these challenges and to describe some practical solutions. A qualitative approach to estimating the level of reservoir depletion from formation gas measurements has been developed for the Jasmine Field, comparing pre-depletion gas trends against those obtained during the infill drilling campaign. The methods described here to estimate depleted fracture gradients using modelled and observed stress paths coupled to the pore pressure reduction were found to fit with well observations, and have helped to inform operational decisions to manage severe lost circulation events. A strategy to acquire data in memory while drilling has proved successful and has allowed lost circulation events to be managed safely. Managed pressure drilling has opened up narrow drilling windows, and has reduced the number of hole sizes and liners required to drill these infill wells.
Thematic collection: This article is part of the Geopressure collection available at: https://www.lyellcollection.org/topic/collections/geopressure
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Volumes & issues
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Volume 30 (2024)
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Volume 29 (2023)
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Volume 28 (2022)
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Volume 27 (2021)
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Volume 26 (2020)
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Volume 25 (2019)
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Volume 24 (2018)
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Volume 23 (2017)
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Volume 22 (2016)
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Volume 21 (2015)
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Volume 20 (2014)
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Volume 19 (2013)
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Volume 18 (2012)
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Volume 17 (2011)
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Volume 16 (2010)
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Volume 15 (2009)
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Volume 14 (2008)
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Volume 13 (2007)
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Volume 12 (2006)
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Volume 11 (2005)
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Volume 10 (2004)
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Volume 9 (2003)
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Volume 8 (2002)
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Volume 7 (2001)
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Volume 6 (2000)
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Volume 5 (1999)
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Volume 4 (1998)
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Volume 3 (1997)
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Volume 2 (1996)
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Volume 1 (1995)