Data Engineering

ALHAN SALMAN

Houston, TX H: 832-***-**** ************@*****.***

Summary

Data Science professional with machine learning skills, delivering scalable and efficient ML products Experience

Jr.Data Scientist - Corva AI - Houston, TX 10/2019 to 03/2020

• Worked on development of data driven analysis and statistical predictive modeling solutions for oil and gas operations to reduce the operations cost and nonproductive time (NPT)

• Extract, transform and load the data (ETL process)

• Performed data analysis, created dashboards and reports

• Developed machine learning algorithm (supervised and unsupervised) to synthesize well logs data to enhance the real-time analytics system in Corva platform utilizing streamed IoT data and got 91% model accuracy

• Performed complex data pre-processing and derive insight from structured and unstructured data

• Monitored and maintained real time operations dashboards

• Performed Corva software test and reported bugs using JIRA

• Utilized Notion to document the life cycle of the software applications building process Data Scientist - FracGeo - The Woodlands, TX 06/2018 to 10/219

• Collected, cleaned for anomalies, analyzed, synthesized and predicted missing data using Artificial Intelligence, Machine Learning and Statistical methods

• Recognizing the anomalies in oil and gas data and correct them using scientific tools

• Identified data trends in geological, geophysical and reservoir data to build 3D reservoir modeling workflow

• Built statistical charts and dashboards to analyze the data, recognize the distribution to perform the engineering calculations

• Tested FracPredictor software applications

Staff Engineer - 3B Engineering - Las Vegas, NV 09/2017 to 05/2018

• Conducted field data evaluation and engineering data analysis

• Prepared geotechnical reports and boring logs

Research Advisor - Global Energy Network at USC - Los Angeles, CA 06/2017 to 09/2017

• Used Neural Network to synthesize well logs data getting 85% accuracy

• Built rock physics models, 3D Gaussian simulation geomechanical models, rock mechanic data analysis

• Analyzed production data to perform reservoir simulation

• Performed uncertainty analysis using Monte Carlo simulation Petroleum Database Engineer - OEC Information Bank - Baghdad, Iraq 05/2006 to 11/2013

• Worked on management information system to digitalize the Iraqi fields data

• Conducted geo-spatial studies (Northern, Middle, Southern parts of Iraq)

• Conducted reservoir studies including reservoir simulation, forecasted reservoir production, economic evaluation and risk assessment, evaluated enhance oil recovery techniques, flow diagram analysis, and uncertainty analysis

• Prepared reports for development plans

Skills

• Programing Skills: Python, SQL, NOSQL,

MongoDB, HTML, CSS, Spark, Flask,

Hadoop, Git, and Github

• Visualization Tools: Tableau, 3D geospatial

modeling tool such as Petrel

• Clouds: Amazon Web Services (EC2, S3,

Lambda, AWS SageMaker)

• Machine Learning: Time Series Analysis,

Deep Learning, Reinforcement Learning,

Neural Network, Computer Vision,

Regression and Natural Language

Processing

• Petroleum Engineering: Reservoir

Simulation such as CMG and Eclipse, Energy

Data Analytics Software Applications such as

Corva

• Communication: Excellent written,

communication, leadership and verbal skills

(English and Arabic)

• Others: Scikit-learn, Keras, PyTorch,

Tensorflow, Pandas, Nump,

Education and Training

Master of Science: Petroleum Engineering 2017

University of Southern California Los Angeles, CA

Bachelor of Science: Petroleum Engineering 2005

University of Baghdad Baghdad, Iraq

Certifications

Fundamentals of Visualization with Tableau, Python for Data Science and AI, SQL for Data Science, AWS Machine Learning Foundations

Activities and Honors

• Winner of Chevron-USC Student Design Competition 2017

• Recipient of Woodruff Petroleum Award Spring 2017 Selected Projects

• Time Series Analysis: Gamma ray at bit estimation to avoid human error in data analysis

• Unsupervised Learning: Modeling integrity of carbon storage to avoid CO2 leakage into the ground water

• Exploratory Data Analysis: Lean green operations utilizing drones monitoring system data analysis to minimize gas flaring

• Supervised Learning: Stress and shear velocity prediction using Multilinear Regression with 91% accuracy to optimize hydraulic fracturing operations

• Principle Component Analysis: Eagle Ford and Niobrara 3D geological modeling

• Deep Learning: Bakken heterogeneity characterization

• Time Series Forecasting: Economic analysis and forecasting

• Deep Learning: Ain Dar fracture optimization

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