Abu Dhabi Company for Onshore Oil Operations

About: Abu Dhabi Company for Onshore Oil Operations is a based out in . It is known for research contribution in the topics: Carbonate & Carbonate rock. The organization has 184 authors who have published 111 publications receiving 1562 citations. The organization is also known as: ADCO.

Corrosion threat assessment of crude oil flow lines using Bayesian network model

Abstract: Abu Dhabi Company for Onshore Oil Operations operates multiple carbon steel oil flow lines, which are emplaced on the desert surface of Abu Dhabi. The company's main oil pipelines are buried with coating and cathodic protection and internally protected by chemical inhibition, but the flow lines are without coating and cathodic protection. Over the years, this approach has been successful for flow lines, but the frequency of corrosion related leaks has increased recently due to changing operating and external conditions. This paper describes the use of a Bayesian network model that combines physics based models and expert knowledge of the flow lines to predict corrosion flaws depth and leak probability. It is shown that the Bayesian network approach can be useful in estimating location specific probability of failure and thus providing input to the prioritisation of inspections and corrosion mitigation. The approach was validated for five selected flow lines, where detailed field examination was av...

Reservoir parameters determination using artificial neural networks: Ras Fanar field, Gulf of Suez, Egypt

Abstract: Ras Fanar field is one of the largest oil-bearing carbonate reservoirs in the Gulf of Suez. The field produces from the Middle Miocene Nullipore carbonate reservoir, which consists mainly of algal-rich dolomite and dolomitic limestone rocks, and range in thickness between 400 and 980 ft. All porosity types within the Nullipore rocks have been modified by diagenetic processes such as dolomitization, leaching, and cementation; hence, the difficulty arise in the accurate determination of certain petrophysical parameters, such as porosity and permeability, using logging data only. In this study, artificial neural networks (ANN) are used to estimate and predict the most important petrophysical parameters of Nullipore reservoir based on well logging data and available core plug analyses. The different petrophysical parameters are first calculated from conventional logging and measured core analyses. It is found that pore spaces are uniform all over the reservoirs (17–23%), while hydrocarbon content constitutes more than 55% and represented mainly by oil with little saturations of secondary gasses. A regular regression analysis is carried out over the calculated and measured parameters, especially porosity and permeability. Fair to good correlation (R 80%) indicating that good matching and correlation is achieved between the measured and predicted parameters. This well-learned artificial neural network can be further applied as a predictive module in other wells in Ras Fanar field where core data are unavailable.