Photoelectric Factor Prediction Using Automated Learning and Uncertainty
Quantification
- URL: http://arxiv.org/abs/2206.08950v1
- Date: Fri, 17 Jun 2022 18:03:38 GMT
- Title: Photoelectric Factor Prediction Using Automated Learning and Uncertainty
Quantification
- Authors: Khalid L. Alsamadony, Ahmed Farid Ibrahim, Salaheldin Elkatatny,
Abdulazeez Abdulraheem
- Abstract summary: The photoelectric factor (PEF) is an important well logging tool to distinguish between different types of reservoir rocks.
The ratio of rock minerals could be determined by combining PEF log with other well logs.
However, PEF log could be missing in some cases such as in old well logs and wells drilled with barium-based mud.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The photoelectric factor (PEF) is an important well logging tool to
distinguish between different types of reservoir rocks because PEF measurement
is sensitive to elements with high atomic number. Furthermore, the ratio of
rock minerals could be determined by combining PEF log with other well logs.
However, PEF log could be missing in some cases such as in old well logs and
wells drilled with barite-based mud. Therefore, developing models for
estimating missing PEF log is essential in those circumstances. In this work,
we developed various machine learning models to predict PEF values using the
following well logs as inputs: bulk density (RHOB), neutron porosity (NPHI),
gamma ray (GR), compressional and shear velocity.
The predictions of PEF values using adaptive-network-fuzzy inference system
(ANFIS) and artificial neural network (ANN) models have errors of about 16% and
14% average absolute percentage error (AAPE) in the testing dataset,
respectively. Thus, a different approach was proposed that is based on the
concept of automated machine learning. It works by automatically searching for
the optimal model type and optimizes its hyperparameters for the dataset under
investigation. This approach selected a Gaussian process regression (GPR) model
for accurate estimation of PEF values. The developed GPR model decreases the
AAPE of the predicted PEF values in the testing dataset to about 10% AAPE. This
error could be further decreased to about 2% by modeling the potential noise in
the measurements using the GPR model.
Related papers
- Model-Based Reparameterization Policy Gradient Methods: Theory and
Practical Algorithms [88.74308282658133]
Reization (RP) Policy Gradient Methods (PGMs) have been widely adopted for continuous control tasks in robotics and computer graphics.
Recent studies have revealed that, when applied to long-term reinforcement learning problems, model-based RP PGMs may experience chaotic and non-smooth optimization landscapes.
We propose a spectral normalization method to mitigate the exploding variance issue caused by long model unrolls.
arXiv Detail & Related papers (2023-10-30T18:43:21Z) - Uncertainty and Explainable Analysis of Machine Learning Model for
Reconstruction of Sonic Slowness Logs [5.815454346817298]
We use data from the 2020 machine learning competition of the SPWLA to predict the missing compressional wave slowness and shear wave slowness logs.
We employ the NGBoost algorithm to construct an Ensemble Learning model that can predicate the results as well as their uncertainty.
Our findings reveal that the NGBoost model tends to provide greater slowness prediction results when the neutron porosity and gamma ray are large.
arXiv Detail & Related papers (2023-08-24T08:03:15Z) - PPG-based Heart Rate Estimation with Efficient Sensor Sampling and
Learning Models [6.157700936357335]
Photoplethysthy (mography) sensors embedded in wearable devices can estimate heart rate (HR) with high accuracy.
However, applying PPG sensor based HR estimation to embedded devices still faces challenges due to the energy-intensive high-frequency PPG sampling.
In this work, we aim to explore HR estimation techniques that are more suitable for lower-power and resource-constrained embedded devices.
arXiv Detail & Related papers (2023-03-23T19:47:36Z) - A Meta-Learning Approach to Predicting Performance and Data Requirements [163.4412093478316]
We propose an approach to estimate the number of samples required for a model to reach a target performance.
We find that the power law, the de facto principle to estimate model performance, leads to large error when using a small dataset.
We introduce a novel piecewise power law (PPL) that handles the two data differently.
arXiv Detail & Related papers (2023-03-02T21:48:22Z) - Estimating oil and gas recovery factors via machine learning:
Database-dependent accuracy and reliability [0.0]
A key reservoir property is hydrocarbon recovery factor (RF) whose accurate estimation would provide decisive insights to drilling and production strategies.
This study aims to estimate the hydrocarbon RF for exploration from various reservoir characteristics, such as porosity, permeability, pressure, and water saturation via the machine learning (ML) approach.
arXiv Detail & Related papers (2022-10-22T16:25:49Z) - Physics-informed machine learning with differentiable programming for
heterogeneous underground reservoir pressure management [64.17887333976593]
Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO2 sequestration and wastewater injection.
Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface.
We use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization.
arXiv Detail & Related papers (2022-06-21T20:38:13Z) - Flexible Amortized Variational Inference in qBOLD MRI [56.4324135502282]
Oxygen extraction fraction (OEF) and deoxygenated blood volume (DBV) are more ambiguously determined from the data.
Existing inference methods tend to yield very noisy and underestimated OEF maps, while overestimating DBV.
This work describes a novel probabilistic machine learning approach that can infer plausible distributions of OEF and DBV.
arXiv Detail & Related papers (2022-03-11T10:47:16Z) - Integration of neural network and fuzzy logic decision making compared
with bilayered neural network in the simulation of daily dew point
temperature [0.8808021343665321]
dew point temperature (DPT) is simulated using the data-driven approach.
Various input patterns, namely T min, T max, and T mean, are utilized for training the architecture.
arXiv Detail & Related papers (2022-02-23T14:25:13Z) - Prediction of liquid fuel properties using machine learning models with
Gaussian processes and probabilistic conditional generative learning [56.67751936864119]
The present work aims to construct cheap-to-compute machine learning (ML) models to act as closure equations for predicting the physical properties of alternative fuels.
Those models can be trained using the database from MD simulations and/or experimental measurements in a data-fusion-fidelity approach.
The results show that ML models can predict accurately the fuel properties of a wide range of pressure and temperature conditions.
arXiv Detail & Related papers (2021-10-18T14:43:50Z) - Artificial Intelligence Hybrid Deep Learning Model for Groundwater Level
Prediction Using MLP-ADAM [0.0]
In this paper, a multi-layer perceptron is applied to simulate groundwater level.
The adaptive moment estimation algorithm is also used to this matter.
Results indicate that deep learning algorithms can demonstrate a high accuracy prediction.
arXiv Detail & Related papers (2021-07-29T10:11:45Z) - Physics-informed CoKriging model of a redox flow battery [68.8204255655161]
Redox flow batteries (RFBs) offer the capability to store large amounts of energy cheaply and efficiently.
There is a need for fast and accurate models of the charge-discharge curve of a RFB to potentially improve the battery capacity and performance.
We develop a multifidelity model for predicting the charge-discharge curve of a RFB.
arXiv Detail & Related papers (2021-06-17T00:49:55Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.