Development of New Models to Determine the Rheological Parameters of Water-Based Drilling Fluid using Artificial Neural Networks

Farqad Hadi; Ali Noori; Hussein Hussein; Ameer Khudhair

Article Details

Development of New Models to Determine the Rheological Parameters of Water-Based Drilling Fluid using Artificial Neural Networks

Authors:

Farqad Hadi | Ali Noori | Hussein Hussein | Ameer Khudhair

Publisher:

Iraqi Geologists Union (Union of Iraqi Geologists)

Journal:

The Iraqi Geological Journal Vol.55 Issue 1C, pp.66-79

Document Type:

Article

Publication Date:

3/2022

Document Language:

English

Document ID (Org No.):

2001836085

pISSN:

2663-8754

Keywords:

Abstract:

 It is well known that drilling fluid is a key parameter for optimizing drilling operations, cleaning the hole, and managing the rig hydraulics and margins of surge and swab pressures. Although the experimental works represent valid and reliable results, they are expensive and time-consuming. In contrast, continuous and regular determination of the rheological fluid properties can perform its essential functions during good construction. The aim of this study is to develop empirical models to estimate the drilling mud rheological properties of water-based fluids with less need for lab measurements. This study provides two predictive techniques, multiple regression analysis, and artificial neural networks, to determine the rheological properties of water-based drilling fluid using other simple measurable properties. While mud density, marsh funnel, and solid% are key input parameters in this study, the output models are plastic viscosity, yield point apparent viscosity and gel strength. The prediction methods have been applied on datasets taken from the final reports of two wells drilled in the Ahdeb oil field, eastern Iraq. To test the performance ability of the developed models, two error-based metrics (determination coefficient R2 and root mean square error have been used in this study. The current results support the evidence that MW, MF, and solid% are consistent indexes for the prediction of rheological mud properties. Both mud density and solid content have a relative-significant effect on increasing PV, YP, AV, and gel strength. The results also reveal that both MRA and ANN are conservative in estimating the fluid rheological properties, but ANN is more precise than MRA. Eight empirical mathematical models with high-performance capacity have been developed in this study to determine the rheological fluid properties using simple and quick equipment such as mud balance and marsh funnel. This study presents cost-effective models to determine.

Article Details

Development of New Models to Determine the Rheological Parameters of Water-Based Drilling Fluid using Artificial Neural Networks

Authors:

Farqad Hadi | Ali Noori | Hussein Hussein | Ameer Khudhair

Publisher:

Iraqi Geologists Union (Union of Iraqi Geologists)

Journal:

The Iraqi Geological Journal Vol.55 Issue 1C, pp.66-79

Document Type:

Article

Publication Date:

3/2022

Document Language:

English

Document ID (Org No.):

2001836085

pISSN:

2663-8754

Plastic viscosity, Yield point

Average viscosity

Gel strength

Artificial neural networks

Issues

Article Details

Development of New Models to Determine the Rheological Parameters of Water-Based Drilling Fluid using Artificial Neural Networks

Authors: Farqad Hadi | Ali Noori | Hussein Hussein | Ameer Khudhair

Publisher: Iraqi Geologists Union (Union of Iraqi Geologists)

Journal: The Iraqi Geological Journal Vol.55 Issue 1C, pp.66-79

Document Type: Article

Publication Date: 3/2022

Document Language: English

Document ID (Org No.): 2001836085

pISSN: 2663-8754

Plastic viscosity, Yield point

Average viscosity

Gel strength

Artificial neural networks

Issues

Authors:

Farqad Hadi | Ali Noori | Hussein Hussein | Ameer Khudhair

Publisher:

Iraqi Geologists Union (Union of Iraqi Geologists)

Journal:

The Iraqi Geological Journal Vol.55 Issue 1C, pp.66-79

Document Type:

Article

Publication Date:

3/2022

Document Language:

English

Document ID (Org No.):

2001836085

pISSN:

2663-8754