Evaporation is one of the major components of the hydrological cycle in the nature, thus its accurate estimation is so important in the planning and management of the irrigation practices and to assess water availability and requirements. The aim of this study is to investigate the ability of fuzzy inference system for estimating monthly pan evaporation form meteorological data. The study has been carried out depending on 261 monthly measurements of each of temperature (T), relative humidity (RH), and wind speed (W) which have been available in Emara meteorological station, southern Iraq. Three different fuzzy models comprising various combinations of monthly climatic variables (temperature, wind speed, and relative humidity) were developed

    Hartha Formation is an overburdened horizon in the X-oilfield which generates a lot of Non-Productive Time (NPT) associated with drilling mud losses. This study has been conducted to investigate the loss events in this formation as well as to provide geological interpretations based on datasets from nine wells in this field of interest. The interpretation was based on different analyses including wireline logs, cuttings descriptions, image logs, and analog data. Seismic and coherency data were also used to formulate the geological interpretations and calibrate that with the loss events of the Hartha Fm.

   The results revealed that the upper part of the Hartha Fm. was identified as an interval capable of creating potentia

Today, there are large amounts of geospatial data available on the web such as Google Map (GM), OpenStreetMap (OSM), Flickr service, Wikimapia and others. All of these services called open source geospatial data. Geospatial data from different sources often has variable accuracy due to different data collection methods; therefore data accuracy may not meet the user requirement in varying organization. This paper aims to develop a tool to assess the quality of GM data by comparing it with formal data such as spatial data from Mayoralty of Baghdad (MB). This tool developed by Visual Basic language, and validated on two different study areas in Baghdad / Iraq (Al-Karada and Al- Kadhumiyah). The positional accuracy was asses

These days, it is crucial to discern between different types of human behavior, and artificial intelligence techniques play a big part in that.  The characteristics of the feedforward artificial neural network (FANN) algorithm and the genetic algorithm have been combined to create an important working mechanism that aids in this field. The proposed system can be used for essential tasks in life, such as analysis, automation, control, recognition, and other tasks. Crossover and mutation are the two primary mechanisms used by the genetic algorithm in the proposed system to replace the back propagation process in ANN. While the feedforward artificial neural network technique is focused on input processing, this should be based on the proce

Precise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables

In recent years, the Global Navigation Satellite Services (GNSS) technology has been frequently employed for monitoring the Earth crust deformation and movement. Such applications necessitate high positional accuracy that can be achieved through processing GPS/GNSS data with scientific software such as BERENSE, GAMIT, and GIPSY-OSIS. Nevertheless, these scientific softwares are sophisticated and have not been published as free open source software. Therefore, this study has been conducted to evaluate an alternative solution, GNSS online processing services, which may obtain this privilege freely. In this study, eight years of GNSS raw data for TEHN station, which located in Iran, have been downloaded from UNAVCO website

Generally, statistical methods are used in various fields of science, especially in the research field, in which Statistical analysis is carried out by adopting several techniques, according to the nature of the study and its objectives. One of these techniques is building statistical models, which is done through regression models. This technique is considered one of the most important statistical methods for studying the relationship between a dependent variable, also called (the response variable) and the other variables, called covariate variables. This research describes the estimation of the partial linear regression model, as well as the estimation of the “missing at random” values (MAR). Regarding the

The physical and elastic characteristics of rocks determine rock strengths in general. Rock strength is frequently assessed using porosity well logs such as neutron and sonic logs. The essential criteria for estimating rock mechanic parameters in petroleum engineering research are uniaxial compressive strength and elastic modulus. Indirect estimation using well-log data is necessary to measure these variables. This study attempts to create a single regression model that can accurately forecast rock mechanic characteristics for the Harth Carbonate Formation in the Fauqi oil field. According to the findings of this study, petrophysical parameters are reliable indexes for determining rock mechanical properties having good performance p