Estimation of shear sonic logs in the heterogeneous and fractured Lower Cretaceous of the Danish North Sea using supervised learning

Lorentzen, Mads; Bredesen, Kenneth; Mosegaard, Klaus; Nielsen, Lars

doi:10.1111/1365-2478.13252

Cited by 3 publications

(3 citation statements)

References 56 publications

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“…We use the Adam optimizer (Kingma and Ba, 2014), rectified linear unit activation function (0 if the input is below 0 and f (x) = x if the input x is above 0) and mean squared error as loss functions. Those three settings have been used widely in regression problems (Braakmann-Folgmann and Donlon, 2019;Liu et al, 2022;Lorentzen et al, 2022).…”

Section: Ann Structure and Trainingmentioning

confidence: 99%

Recent warming trends of the Greenland ice sheet documented by historical firn and ice temperature observations and machine learning

Vandecrux,

Fausto,

Box

et al. 2024

The Cryosphere

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Abstract. Surface melt on the Greenland ice sheet has been increasing in intensity and extent over the last decades due to Arctic atmospheric warming. Surface melt depends on the surface energy balance, which includes the atmospheric forcing but also the thermal budget of the snow, firn and ice near the ice sheet surface. The temperature of the ice sheet subsurface has been used as an indicator of the thermal state of the ice sheet's surface. Here, we present a compilation of 4612 measurements of firn and ice temperature at 10 m below the surface (T10 m) across the ice sheet, spanning from 1912 to 2022. The measurements are either instantaneous or monthly averages. We train an artificial neural network model (ANN) on 4597 of these point observations, weighted by their relative representativity, and use it to reconstruct T10 m over the entire Greenland ice sheet for the period 1950–2022 at a monthly timescale. We use 10-year averages and mean annual values of air temperature and snowfall from the ERA5 reanalysis dataset as model input. The ANN indicates a Greenland-wide positive trend of T10 m at 0.2 ∘C per decade during the 1950–2022 period, with a cooling during 1950–1985 (−0.4 ∘C per decade) followed by a warming during 1985–2022 (+0.7 ∘ per decade). Regional climate models HIRHAM5, RACMO2.3p2 and MARv3.12 show mixed results compared to the observational T10 m dataset, with mean differences ranging from −0.4 ∘C (HIRHAM) to 1.2 ∘C (MAR) and root mean squared differences ranging from 2.8 ∘C (HIRHAM) to 4.7 ∘C (MAR). The observation-based ANN also reveals an underestimation of the subsurface warming trends in climate models for the bare-ice and dry-snow areas. The subsurface warming brings the Greenland ice sheet surface closer to the melting point, reducing the amount of energy input required for melting. Our compilation documents the response of the ice sheet subsurface to atmospheric warming and will enable further improvements of models used for ice sheet mass loss assessment and reduce the uncertainty in projections.

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Section: Ann Structure and Trainingmentioning

confidence: 99%

Recent warming trends of the Greenland ice sheet documented by historical firn and ice temperature observations and machine learning

Vandecrux,

Fausto,

Box

et al. 2024

The Cryosphere

View full text Add to dashboard Cite

show abstract

Section: Ann Structure and Trainingmentioning

confidence: 99%

Historical snow and ice temperature observations document the recent warming of the Greenland ice sheet

Vandecrux¹,

Fausto²,

Box³

et al. 2023

Preprint

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Abstract. The surface melting of the Greenland ice sheet has been increasing over the last decades due to Arctic atmospheric warming. Surface melt depends on the energy balance which includes the atmospheric forcing but also the thermal budget of the snow, firn and ice near the ice sheet surface. We present a compilation of more than 4500 measurements of ice, snow and firn temperature at 10 m below the surface (T10m) across Greenland spanning from 1912 to 2022. The measurements are either instantaneous or monthly averages. We train an Artificial Neural Network model (ANN) on these observations, weighted by their relative representativity, and use it to reconstruct T10m over the entire Greenland ice sheet. We use 10 year averages and mean annual values of air temperature and snowfall from the ERA5 reanalysis dataset (1950–2022) as model input. The ANN indicates a Greenland-wide positive trend of T10m at 0.2 °C decade-1 during the 1950–2022 period, with a cooling during 1950–1985 (-0.3 °C decade-1) followed by a warming during 1985–2022 (+0.7 °C decade-1). Regional climate models HIRHAM5, RACMO2.3p2 and MARv3.12 show mixed results compared to the observational T10m dataset with mean differences ranging from -0.4 °C (HIRHAM) to 1.3 °C (MAR) and root mean squared differences ranging from 2.8 °C (HIRHAM) to 4.7 °C (MAR). The corresponding values for the ANN are -0.2 °C and 1.7 °C. The observation-based ANN also reveals an underestimation of the subsurface warming trends in climate models for the bare ice and dry snow areas. The subsurface warming brings the Greenland ice sheet surface closer to the melting point, reducing the amount of summer energy input required for melting. Our compilation documents the response of the ice sheet subsurface to atmospheric warming and will enable further improvements of models used for ice sheet mass loss assessment and reduce the uncertainty in projections.

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“…Researchers employed different ML methods, including an artificial neural network, fuzzy logic, functional network, etc. to predict and analyze geomechanical properties [38][39][40][41][42][43][44][45][46]. The studies reveal that different types of formation differ significantly in behavior, and rock mechanical properties are more complex than ideal materials.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Model in Predicting Geomechanical Properties for Shale Formation: A Field Case in Permian Basin

et al. 2022

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Due to complexities in geologic structure, heterogeneity, and insufficient borehole information, shale formation faces challenges in accurately estimating the elastic properties of rock which triggers severe technical challenges in safe drilling and completion. These geomechanical properties could be computed from acoustic logs, however, accurate estimation is critical due to log deficit and a higher recovery expense of inadequate datasets. To fill the gap, this study focuses on predicting the sonic properties of rock using deep neural network (Bi-directional long short-time memory, Bi-LSTM) and random forest (RF) algorithms to estimate and evaluate the geomechanical properties of the potential unconventional formation, Permian Basin, situated in West Texas. A total of three wells were examined using both single-well and cross-well prediction algorithms. Log-derived single-well prediction models include a 75:25 ratio for training and testing the data whereas the cross-well includes two wells for training and the remaining well was used for testing. The selected well input logs include compressional wave slowness, resistivity, gamma-ray, porosity, and bulk density to predict shear wave slowness. The results using RF and Bi-LSTM show a promising prediction of geomechanical properties for Permian Basin wells. RF algorithm performed superior for both single and grouped well prediction. The single-well prediction method using the RF algorithm provided the highest accuracy of 99.90% whereas Bi-LSTM gave 93.60%. The best accuracy for a grouped well prediction was achieved employing Bi-LSTM and RF models, i.e., 96.01% and 93.80%. The average prediction including RF and Bi-LSTM algorithms demonstrated that accuracy for single well and cross well prediction is 96% and 94% respectively with an error below 7%. These outcomes show the astonishing capability of artificial intelligence (AI) models trained to create a realistic prediction to unlock unconventional potential when datasets are inadequate. Given adequate training data, operators could leverage these efficient tools by utilizing them to examine fracture interpretations with reduced cost and time when datasets are incomplete and thus increase the hydrocarbon recovery potential.

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Estimation of shear sonic logs in the heterogeneous and fractured Lower Cretaceous of the Danish North Sea using supervised learning

Cited by 3 publications

References 56 publications

Recent warming trends of the Greenland ice sheet documented by historical firn and ice temperature observations and machine learning

Recent warming trends of the Greenland ice sheet documented by historical firn and ice temperature observations and machine learning

Historical snow and ice temperature observations document the recent warming of the Greenland ice sheet

Artificial Intelligence Model in Predicting Geomechanical Properties for Shale Formation: A Field Case in Permian Basin

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