Machine learning application to predict in-situ stresses from logging data

Determination of in-situ stresses is essential for subsurface planning and modeling, such as horizontal well planning and hydraulic fracture design. In-situ stresses consist of overburden stress (σv), minimum (σh), and maximum (σH) horizontal stresses. The σh and σH are difficult to determine, whereas the overburden stress can be determined directly from the density logs. The σh and σH can be estimated either from borehole injection tests or theoretical finite elements methods. However, these methods are complex, expensive, or need unavailable tectonic stress data. This study aims to apply different machine learning (ML) techniques, specifically, random forest (RF), functional network (FN), and adaptive neuro-fuzzy inference system (ANFIS), to predict the σh and σH using well-log data. The logging data includes gamma-ray (GR) log, formation bulk density (RHOB) log, compressional (DTC), and shear (DTS) wave transit-time log. A dataset of 2307 points from two wells (Well-1 and Well-2) was used to build the different ML models. The Well-1 data was used in training and testing the models, and the Well-2 data was used to validate the developed models. The obtained results show the capability of the three ML models to predict accurately the σh and σH using the well-log data. Comparing the results of RF, ANFIS, and FN models for minimum horizontal stress prediction showed that ANFIS outperforms the other two models with a correlation coefficient (R) for the validation dataset of 0.96 compared to 0.91 and 0.88 for RF, and FN, respectively. The three models showed similar results for predicting maximum horizontal stress with R values higher than 0.98 and an average absolute percentage error (AAPE) less than 0.3%. a20 index for the actual versus the predicted data showed that the three ML techniques were able to predict the horizontal stresses with a deviation less than 20% from the actual data. For the validation dataset, the RF, ANFIS, and FN models were able to capture all changes in the σh and σH trends with depth and accurately predict the σh and σH values. The outcomes of this study confirm the robust capability of ML to predict σh and σH from readily available logging data with no need for additional costs or site investigation.

Laboratory-Based Correlation between Liquefaction Resistance and Shear Wave Velocity of Sand with Fines

This paper presents the results of a laboratory investigation into the effect of non-plastic fines on the correlation between liquefaction resistance and the shear wave velocity of sand. For this purpose, undrained stress-controlled cyclic triaxial and bender element tests were performed on clean sand and its mixtures with non-plastic silt. It is shown that the correlation between liquefaction resistance and shear wave velocity depends on fines content and confining effective stress. Based on the test results, correlation curves between field liquefaction resistance and overburden stress corrected shear wave velocity for sand containing various contents of fines are derived. These curves are compared to other previously proposed by field and laboratory studies.

Caring ability of urban and rural family caregivers: association with overburden, stress and coping

ABSTRACT Objective: To correlate caring ability with overburden, stress and coping of urban and rural family caregivers of patients undergoing cancer treatment. Method: Cross-sectional study, carried out in a referral hospital for cancer treatment, with urban and rural caregivers who responded the following instruments: questionnaire of sociodemographic characterization of the caregiver and the care provided, Perceived Stress scale, Burden Interview scale and Brief COPE. Pearson's correlation test was used for statistical analysis, with a significance level ≤5%. Results: A total of 163 urban caregivers and 59 rural caregivers participated in the study. Between the caring ability and stress, a negative and moderate correlation was found in rural caregivers. In the relationship between the caring ability and the overburden, there was a statistically significant correlation in urban caregivers in the interpersonal relationship and perception of self-efficacy factor. Between coping and the caring ability, a positive and moderate correlation was identified in coping focused on the problem in the knowledge dimension in urban caregivers. Conclusion: Urban caregivers had greater intensity of overburden and coping focused on the problem in relation to the caring ability.

A New Method for the Estimation of Hydraulic Permeability, Coefficient of Consolidation, and Soil Fraction Based on the Dilatometer Tests (DMT)

The main issue of the paper is the estimation of soil hydraulic permeability based on the DMT test. DMTA, DMTC and SASK methods performed in the Nielisz dam, Stegny and the SGGW Campus of the Warsaw University of Life Sciences sites are described. The article presents the implementation of the dilatometer Marchetti test (DMT) in the determination of soil fraction and effects of its occurrence in the subsoil, tested in the Nielisz dam located in the Wieprz river valley in the Lublin province, and in various sites in Warsaw (Stegny site and SGGW Campus of the Warsaw University of Life Sciences). In order to acquire the needed data, the flat dilatometer test (DMT) method was used. A direct and indirect pressure methodology of interpreting soil swelling was characterized in the article. The paper shows the possibilities of determining sand, silt and clay soil fractions based on po and p1 pressures from dilatometer tests (DMT) and the effective (σ’vo) and total (σvo) vertical in situ overburden stress. Additionally, the main advantage of this paper is the proposal of use of a new chart to determine hydraulic permeability and soil fraction, based on DMT tests.