Nonparametric Multivariate Model Research Articles

Development of Oil Fields Using Science Artificial Intelligence and Machine Learning

Since artificial intelligence has become increasingly prevalent in the oil industry, it is relevant to this study since it is being used for exploration, development, production, field design, and management planning to improve decision-making, reduce costs, and speed up production. For establishing relationships between complex non-linear datasets, machine learning has proved superior to regression methods in petroleum engineering when it comes to high-dimensional data prediction errors, processing power, and memory. In this article, machine learning is compared with conventional statistical models of oil and gas engineering for determining and predicting reservoir pressure values in the development of oil fields. The effectiveness and potential of machine learning to determine reservoir pressure values was analysed. Using non-parametric multivariate model that link well performance over time, a new method is proposed for predicting reservoir pressure using machine learning. According to the proposed method, the predicted reservoir pressure correlates well with values measured by hydrodynamic studies of wells based on the dynamics of indicators describing well performance. Machine learning method based on random forest algorithm tends to provide better prediction reliability for reservoir pressure than linear regression method (absolute deviation: 0.86; relative deviation: 6.8%).

Association of Neurological Impairment on the Relative Benefit of Maximal Extent of Resection in Chemoradiation-Treated Newly Diagnosed Isocitrate Dehydrogenase Wild-Type Glioblastoma.

Increases in the extent of resection of both contrast-enhanced (CE) and non-contrast-enhanced (NCE) tissue are associated with substantial survival benefits in patients with isocitrate dehydrogenase wild-type glioblastoma. The fact, however, remains that these lesions exist within the framework of complex neural circuitry subserving cognition, movement, and behavior, all of which affect the ultimate survival outcome. The prognostic significance of the interplay between CE and NCE cytoreduction and neurological morbidity is poorly understood. To identify a clinically homogenous population of 228 patients with newly diagnosed isocitrate dehydrogenase wild-type glioblastoma, all of whom underwent maximal safe resection of CE and NCE tissue and adjuvant chemoradiation. We then set out to delineate the competing interactions between resection of CE and NCE tissue and postoperative neurological impairment with respect to overall survival. Nonparametric multivariate models of survival were generated via recursive partitioning to provide a clinically intuitive framework for the prognostication and surgical management of such patients. We demonstrated that the presence of a new postoperative neurological impairment was the key factor in predicting survival outcomes across the entire cohort. Patients older than 60 yr who suffered from at least one new impairment had the worst survival outcome regardless of extent of resection (median of 11.6 mo), whereas those who did not develop a new impairment had the best outcome (median of 28.4 mo) so long as all CE tissue was resected. Our data provide novel evidence for management strategies that prioritize safe and complete resection of CE tissue.

A machine learning methodology for multivariate pore-pressure prediction

Accurate pore-pressure prediction is of essential importance to hydrocarbon exploration and development. A multivariate prediction model of multiple petrophysical data is required to adequately reflect variations in pore pressure. However, the parametric multivariate models with assumptions on lithology, predominantly sand or shale, are theoretically inaccurate for mixed lithologies and require a tedious calibration process. Here, we propose a new method of utilizing machine learning (ML) techniques for pore-pressure prediction with a nonparametric multivariate model of petrophysical properties (sonic velocity, porosity, and shale volume). The training dataset for the ML models is constructed using petrophysical properties extracted from well log data and theoretical effective stress in the normally compacted interval. Bowers’ unloading relation is invoked herein to account for abnormal pressure generated by unloading. Four ML algorithms, including the multilayer perceptron neural network, support vector machine, random forest, and gradient boosting machine, are applied to well log data from a set of offshore exploration wells. The results suggest that the proposed method using ML makes pore-pressure predictions in good agreement with pore-pressure measurement, and random forest outperforms the other ML algorithms in terms of goodness-of-fit, generalizability, and prediction accuracy. Compared with methods based on parametric models, the proposed method based on ML produces more accurate pore-pressure prediction and better capture the onset of overpressure.

The effect of regular dental cast artifacts on the 3D superimposition of serial digital maxillary dental models

Superimpositions of serial 3D dental surface models comprise a powerful tool to assess morphological changes due to growth, treatment, or pathology. In this study, we evaluated the effect of artifacts on the superimposition outcome, using standard model acquisition and superimposition techniques. Ten pre- and post-orthodontic treatment plaster models were scanned with an intraoral scanner and superimposed using the iterative closest point algorithm. We repeated the whole process after manual removal of plaster artifacts, according to the current practice, as well as after re-scanning the cleaned models, to assess the effect of the model acquisition process derived artifacts on the superimposition outcome. Non-parametric multivariate models showed no mean effect on accuracy and precision by software settings, cleaning status (artifact removal), or time point. The choice of the superimposition reference area was the only factor that affected the measurements. However, assessment of individual cases revealed significant differences on the detected tooth movement, depending on artifact removal and on the model acquisition process. The effects of all factors tended to decrease with an increase in the size of the superimposition reference area. The present findings highlight the importance of accurate, artifact-free models, for valid assessment of morphological changes through serial 3D model superimpositions.

The influence of climate on the distribution of lichens: a case study in a borderline area (Liguria, NW Italy)

The purposes of this article are to quantify the relationship between epiphytic lichen distribution and macroclimatic variables in the study area and to provide a case study for evaluating the predictive role of epiphytic lichens as bioclimatic indicators. The study was carried out in the Liguria region (NW-Italy), a small (5432 km2) borderline area, where phytoclimatic features range from the dry Mediterranean to the Alpine in a few kilometers. Epiphytic lichen diversity was sampled using a standardized protocol [Asta et al (2002) In: Nimis et al (eds) Monitoring with lichens: monitoring lichens. Kluwer, Dordrecht]. Abundance of the species in the sampling sites was related to macroclimatic parameters (yearly average temperature and rainfall) and non-parametric multivariate models were calculated to find significative relationships among predictive and response variables. A total of 59 species showed highly significant relation with macroclimatic parameters. Four groups were selected, by means of a cluster analysis, related to four climatic niches (warm-humid, cold-humid, mesothermic-humid, warm-dry). Distributional pattern of the groups in the survey area showed a good correspondence with the bioclimatic units of Liguria region described by Nimis [(2003) Checklist of the Lichens of Italy 3.0. University of Trieste, Dept of Biology. http://www.dbiodbs.univ.trieste.it. Cited 1 Jun 2006]. A significant subset of epiphytic lichen species in the study area have been proved to be efficient bioclimatic indicator and it is supposed to give good results to monitor climatic changes, in a long-term perspective.

Probability map of the next M ≥ 5.5 earthquakes in Italy

The main goal of this work is to provide a probability map for the next moderate to large earthquakes (M ≥ 5.5) in Italy. For this purpose we apply a new nonparametric multivariate model to characterize the spatiotemporal distribution of earthquakes. The method is able to account for tectonics/physics parameters that can potentially influence the spatiotemporal variability and tests their relative importance; moreover, it allows straightforward testing of a variety of hypothesis, such as Seismic Gap, Cluster, and Poisson hypothesis. The method has been applied to Italian seismicity of the last four centuries for earthquakes with M ≥ 5.5. Italy has been divided into 61 irregular zones representing areas with homogeneous tectonic regime resulting from active stress data. Besides the magnitude and the time of the earthquakes, the model includes information on the tectonic stress regime, the homogeneity of its orientation, the number of active faults, the dimension of the area and the homogeneity of the topography. The time distribution of the M ≥ 5.5 earthquakes appears clustered in time for a few years after an event, and then the distribution becomes similar to a memoryless Poisson process, leading to a time‐dependent probability map. This map shows that the most likely regions where the next moderate to large earthquakes may occur are Friuli, Umbria‐Marche, and part of Southern Apennines and the Calabrian arc.

A non-parametric hazard model to characterize the spatio-temporal occurrence of large earthquakes; an application to the Italian catalogue

SUMMARY A new non-parametric multivariate model is provided to characterize the spatio-temporal distribution of large earthquakes. The method presents several advantages compared to other more traditional approaches. In particular, it allows straightforward testing of a variety of hypotheses, such as any kind of time dependence (i.e. seismic gap, cluster, and Poisson hypotheses). Moreover, it may account for tectonics/physics parameters that can potentially influence the spatio-temporal variability, and tests their relative importance. The method has been applied to the Italian seismicity of the last four centuries. The results show that large earthquakes in Italy tend to cluster; the instantaneous probability of occurrence in each area is higher immediately after an event and decreases until it reaches, in few years, a constant value representing the average rate of occurrence for that zone. The results also indicate that the clustering is independent of the magnitude of the earthquakes. Finally, a map of the probability of occurrence for the next large earthquakes in Italy is provided.