Saudi Arabian Oil Company

Abdullah Mohammed Alabdullatif

Muhammad Idris

Serkan Dursun

Sarafudheen M Veettil Tharayi

Mishaal Awwadh Alhetairshi

Yaqeen A Mahdi

George Andrew Wattley

The present disclosure describes system and methods for accessing data from a gas oil separation plant (GOSP) facility, wherein the data includes measurements at various locations inside the GOSP facility and measurements of water cut of the GOSP facility; selecting, based on feature engineering, a subset of features corresponding to the measurements at various locations inside the GOSP facility, wherein the subset of features are more likely to impact the water cut of the GOSP facility than unselected features; and based on the subset of features, training a predictive model capable of predicting the water cut of the GOSP facility based on the measurements of water cut of the GOSP facility, wherein the training is based on, at least in part, (i) a subset of the measurements at various locations inside the GOSP facility and (ii) a subset of the measurements of water cut of the GOSP facility.

Gas-oil separation plant virtual water cut predictor based on supervised learning framework on time series data

Systems and methods include a method for providing plots for challenges, successes, and failures in well completions. A challenges-successes-failures database is created from historical data collected from past well completions. The database identifies: 1) challenges encountered during well completions, 2) corresponding successes and failures, and 3) job parameters used during well completions. A dimension embedding algorithm is selected to represent the data. Hyper-parameter tuning is performed on the algorithm. The dimension embedding model is generated and added to a system pipeline for a new well completion job. Nonlinear dimension embedding algorithms are run against data points in the cleaned and processed data using the challenges-successes-failures database and new job parameters entered in a user interface. Scatter plots of two-dimensional (2D) points are generated and labeled with each point's job parameters.

Well completion operations intelligent advisory based on dimension embedding

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Dhahran, Saudi Arabia

Abdullah Mohammed Alabdullatif