Automating the Log Interpretation Workflow Using Machine Learning

2021 ◽  
Author(s):  
Bhuvaneswari Sankaranarayanan ◽  
Aria Abubakar ◽  
David F. Allen ◽  
Ivan Diaz Granados

Abstract Log interpretation is the task of analyzing and processing well logs to generate the subsurface properties around wells. A direct application of machine learning (ML) to this task is to train an ML model for predicting properties in target wells given well logs (data) and properties (labels) in a set of training wells in the same field and/or region. Our ML model of choice for predicting the desired properties is the decision tree-based learning algorithm called random forests (RF). We also devise a mechanism to automatically tune the hyperparameters of this algorithm depending on the data in the training wells. This eliminates the tedious task of carefully tuning the hyperparameters for every new set of training wells and provides a one-click solution. In addition to predicting the properties, we compute the uncertainty in the predicted properties in the form of prediction intervals using the concept of quantile regression forests (QRF). We test our workflow on two use cases. First, we consider a petrophysics use case on an unconventional land dataset to predict the petrophysical properties such as water saturation, total porosity, volume of clay, and total organic carbon from petrophysics logs. Then, we consider a geomechanics use case on a conventional offshore dataset to predict the lithology, pore pressure, and rock mechanical properties. We obtain a good prediction performance on both use cases. The uncertainty estimates also complement the ML model's prediction of the properties by explaining the various correlations that are found to be existing among them based on domain knowledge. The entire workflow of automating the tuning of hyperparameters and training the ML model to predict the properties along with its estimate of uncertainty provide a complete solution to apply the ML workflow for automated log interpretation.

Author(s):  
R. Lalitha ◽  
B. Latha ◽  
G. Sumathi

The success of information system process depends on accuracy of software estimation. Estimation is done at initial phase of software development. It requires a collection of all relevant required information for estimating the software effort. In this paper, a methodology is proposed to maintain a knowledgeable use case repository to store the use cases of various projects in several software project-related domains. This acts as a reference model to compare similar use cases of similar types of projects. The use case points are calculated and using this, schedule estimation and effort estimation of a project are calculated using the formulas of software engineering. These values are compared with the estimated effort and scheduled effort of a new project under development. Apart from these, the effective machine learning technique called neural network is used to measure how accurately the information is processed by use of case repository framework. The proposed machine learning-based use case repository system helps to estimate and analyze the effort using the machine learning algorithms.


Lung cancer is one of the diseases which has a high mortality. If the condition is detected earlier, then it is easier to reduce the mortality rate. This lung cancer has caused more deaths in the world than any other cancer. The main objective is to predict lung cancer using a machine learning algorithm. Several computer-aided systems have been designed to reduce the mortality rate due to lung cancer. Machine learning is a promising tool to predict lung cancer in its early phase or stage, where the features of images are trained using a classification model. Generally, machine learning is used to have a good prediction, but in some models, due to lack of efficient feature extraction value, the training has not been done more effectively; hence the predictions are poor. In order to overcome this limitation, the proposed covariant texture model utilizing the steerable Riesz wavelets feature extraction technique to increase the effectiveness of training via the Random Forest algorithm. In this proposed model, the RF algorithm is employed to predict whether the nodule in the image is benign or malignant ii) to find the level of severity (1 to 5), if it is a malignant nodule. Our experiment result can be used as a tool to support the diagnosis and to analyze at an earlier stage of cancer to cure it.


2020 ◽  
pp. 1362-1369
Author(s):  
Gheed Chaseb ◽  
Thamer A. Mahdi

This study aims to evaluate reservoir characteristics of Hartha Formation in Majnoon oil field based on well logs data for three wells (Mj-1, Mj-3 and Mj-11). Log interpretation was carried out by using a full set of logs to calculate main petrophysical properties such as effective porosity and water saturation, as well as to find the volume of shale. The evaluation of the formation included computer processes interpretation (CPI) using Interactive Petrophysics (IP) software.  Based on the results of CPI, Hartha Formation is divided into five reservoir units (A1, A2, A3, B1, B2), deposited in a ramp setting. Facies associations is added to well logs interpretation of Hartha Formation, and was inferred by a microfacies analysis of thin sections from core and cutting samples. The CPI shows that the A2 is the main oil- bearing unit, which is characterized by good reservoir properties, as indicated by high effective porosity, low water saturation, and low shale volume. Less important units include A1 and A3, because they have low petrophysical properties compared to the unit A2.


2021 ◽  
pp. 1-14
Author(s):  
Ahmed Farid Ibrahim ◽  
Salaheldin Elkatatny ◽  
Yasmin Abdelraouf ◽  
Mustafa Al Ramadan

Abstract Water saturation (Sw) is a vital factor for the hydrocarbon in-place calculations. Sw is usually calculated using different equations; however, its values have been inconsistent with the experimental results due to often incorrectness of their underlying assumptions. Moreover, the main hindrance remains in these approaches due to their strong reliance on experimental analysis which are expensive and time-consuming. This study introduces the application of different machine learning (ML) methods to predict Sw from the conventional well logs. Function networks (FN), support vector machine (SVM), and random forests (RF) were implemented to calculate the Sw using gamma-ray (GR) log, Neutron porosity (NPHI) log, and resistivity (Rt) log. A dataset of 782 points from two wells (Well-1 and Well-2) in tight gas sandstone formation was used to build and then validate the different ML models. The data set from Well-1 was applied for the ML models training and testing, then the unseen data from well-2 was used to validate the developed models. The results from FN, SVM and RF models showed their capability of accurately predicting the Sw from the conventional well logging data. The correlation coefficient (R) values between actual and estimated Sw from the FN model were found to be 0.85 and 0.83 compared to 0.98, and 0.95 from the RF model in the case of training and testing sets, respectively. SVM model shows an R-value of 0.95 and 0.85 in the different datasets. The average absolute percentage error (AAPE) was less than 8% in the three ML models. The ML models outperform the empirical correlations that have AAPE greater than 19%. This study provides ML applications to accurately forecast the water saturation using the readily available conventional well logs without additional core analysis or well site interventions.


2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  

2020 ◽  
pp. 1-12
Author(s):  
Li Dongmei

English text-to-speech conversion is the key content of modern computer technology research. Its difficulty is that there are large errors in the conversion process of text-to-speech feature recognition, and it is difficult to apply the English text-to-speech conversion algorithm to the system. In order to improve the efficiency of the English text-to-speech conversion, based on the machine learning algorithm, after the original voice waveform is labeled with the pitch, this article modifies the rhythm through PSOLA, and uses the C4.5 algorithm to train a decision tree for judging pronunciation of polyphones. In order to evaluate the performance of pronunciation discrimination method based on part-of-speech rules and HMM-based prosody hierarchy prediction in speech synthesis systems, this study constructed a system model. In addition, the waveform stitching method and PSOLA are used to synthesize the sound. For words whose main stress cannot be discriminated by morphological structure, label learning can be done by machine learning methods. Finally, this study evaluates and analyzes the performance of the algorithm through control experiments. The results show that the algorithm proposed in this paper has good performance and has a certain practical effect.


2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.


Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.


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