Real-time determination of rheological properties of high over-balanced drilling fluid used for drilling ultra-deep gas wells using artificial neural network

Gomaa, Ibrahim; Elkatatny, Salaheldin; Abdulraheem, Abdulazeez

doi:10.1016/j.jngse.2020.103224

Cited by 30 publications

(23 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the above theories, artificial intelligence methods are used to estimate rheological properties more accurately in real time based on parameters such as the Marsh funnel viscosity, mud weight, and solid content [ 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 ]. Bispo et al [ 72 ] used temperature, xanthan gum, bentonite, and barite to estimate AV.…”

Section: Real-time Measurement Technologiesmentioning

confidence: 99%

Real-Time Measurement of Drilling Fluid Rheological Properties: A Review

Liu

Zhang

Gao

et al. 2021

Sensors

View full text Add to dashboard Cite

The accurate and frequent measurement of the drilling fluid’s rheological properties is essential for proper hydraulic management. It is also important for intelligent drilling, providing drilling fluid data to establish the optimization model of the rate of penetration. Appropriate drilling fluid properties can improve drilling efficiency and prevent accidents. However, the drilling fluid properties are mainly measured in the laboratory. This hinders the real-time optimization of drilling fluid performance and the decision-making process. If the drilling fluid’s properties cannot be detected and the decision-making process does not respond in time, the rate of penetration will slow, potentially causing accidents and serious economic losses. Therefore, it is important to measure the drilling fluid’s properties for drilling engineering in real time. This paper summarizes the real-time measurement methods for rheological properties. The main methods include the following four types: an online rotational Couette viscometer, pipe viscometer, mathematical and physical model or artificial intelligence model based on a Marsh funnel, and acoustic technology. This paper elaborates on the principle, advantages, limitations, and usage of each method. It prospects the real-time measurement of drilling fluid rheological properties and promotes the development of the real-time measurement of drilling rheological properties.

show abstract

Section: Real-time Measurement Technologiesmentioning

confidence: 99%

Real-Time Measurement of Drilling Fluid Rheological Properties: A Review

Liu

Zhang

Gao

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Gowida 45 developed another ANN model for predicting WBM plastic viscosity and apparent viscosity using mud weight and marsh funnel viscosity as input parameters, and 200 actual datasets were employed to develop such a model. Gomaa 13 Since the petroleum industry deals with massive datasets in almost all its aspects including drilling, reservoir engineering, production, and even exploration, 17 it was vital to employ artificial intelligent techniques to cluster, classify, or even in regression analysis to establish intelligent models that can function by itself with a high degree of accuracy due to their excellent ability to capture complex nonlinear patterns within any data bank. 56−58 One of these techniques is artificial neural network, which proved its potential in many fields.…”

Section: Conventional Drilling Fluids Modelsmentioning

confidence: 99%

“…7−9 Certainly, under these conditions, mud rheology would be altered and may result in borehole challenges including well control issues, hole cleaning, lost circulation, and more undesirable incidents; 6,9−11 apparent viscosity (AV), yield point (YP), gel strength besides, flow behavior index (n), and flow consistency (K) are normally measured once every 12 h, while for mud density, marsh funnel viscosity and solid content are ideally measured each 10−20 min. 6,9,12,13 Rheological properties are conventionally estimated via a viscosimeter, which is tedious and timeconsuming; however, its measurements are frequently required to ensure mud quality while drilling. Hence, robust real-time estimation of mud rheology is compelling.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Rheological Properties of Invert Emulsion Mud Using an Artificial Neural Network

et al. 2021

View full text Add to dashboard Cite

Successful drilling operations require optimum well planning to overcome the challenges associated with geological and environmental constraints. One of the main well design programs is the mud program, which plays a crucial role in each drilling operation. Researchers focus on modeling the rheological properties of the drilling fluid seeking for accurate and real-time predictions that confirm its crucial potential as a research point. However, only substantial studies have real impact on the literature. Several AI-based models have been proposed for estimating mud rheological properties. However, most of them suffer from nonbeing field applicable attractive due to using non-readily field parameters as input variables. Some other studies have not provided a comprehensive description of the model to replicate or reproduce results using other datasets. In this study, two novel robust artificial neural network (ANN) models for estimating invert emulsion mud plastic viscosity and yield point have been developed using actual field data based on 407 datasets. These datasets include mud plastic viscosity (PV), yield point (YP), mud temperature (T), marsh funnel viscosity (MF), and solid content. The mathematical base of each model has been provided to provide a clear means for models' replicability. Results of the evaluation criteria depicted the outstanding performance and consistency of the proposed models over extant ANN models and empirical correlations. Statistical evaluation revealed that the plastic viscosity ANN model has a coefficient of determination (R 2 ) of 98.82%, a root-mean-square error (RMSE) of 1.37, an average relative error (ARE) of 0.12, and an absolute average relative error of 2.69, while for yield point, this model has a coefficient of determination (R 2 ) of 94%, a root-mean-square error (RMSE) of 0.76, an average relative error (ARE) of −0.67, and an absolute average relative error of 3.18.

show abstract

“…Zhu Z et al [23] predicted the settlement behavior of rod proppant in fractures through the artificial neural network, and the data set was from 588 practical laboratory experiments. Aiming at the advantages of machine learning methods in data processing, it can improve more accurate guidance for learning more data [24][25][26]. Machine learning methods seem to be able to replace the methods recommended by the experience of field engineers.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Control Effect of Fractured Leakage in Unconventional Reservoirs Using Machine Learning Method

Song

et al. 2022

Geofluids

View full text Add to dashboard Cite

Bridging plugging is the most used method of plugging in unconventional oil reservoirs, and many factors affect the effect of bridging and plugging. Since the laboratory cannot simulate the actual leakage size of the lost formation and the corresponding leakage plugging process at the drilling site, the laboratory experiment results cannot reflect the actual leakage plugging construction effect. Aiming at the problem of frequent fracture leakage during drilling in Chepaizi block, Xinjiang, China, this paper proposes a set of machine learning methods based on a neural network. Three types of factors and 14 parameters with a strong correlation with the leakage control effect were screened out. Three categories of factors include construction parameters, choice of plugging material, and fluid properties of the carrier fluid. The training was carried out based on the collected field data, the appropriate activation function was set, and the deep well network structure was optimized. By improving the field plugging measures in the later period, the model was verified by these actual cases, and the results showed that the established model produced the highest R 2 of 0.974, has a good fit, and predicts well.

show abstract

Real-time determination of rheological properties of high over-balanced drilling fluid used for drilling ultra-deep gas wells using artificial neural network

Cited by 30 publications

References 23 publications

Real-Time Measurement of Drilling Fluid Rheological Properties: A Review

Real-Time Measurement of Drilling Fluid Rheological Properties: A Review

Prediction of the Rheological Properties of Invert Emulsion Mud Using an Artificial Neural Network

Prediction of the Control Effect of Fractured Leakage in Unconventional Reservoirs Using Machine Learning Method

Contact Info

Product

Resources

About