Real-Time Drilling Fluid Monitoring and Analysis - Adding to Integrated Drilling Operations

2012 ◽  
Author(s):  
Egil Ronaes ◽  
Truls Hosy Fossdal ◽  
Tore Stock
Keyword(s):  
Real Time ◽  
Drilling Fluid ◽  
Drilling Operations

Technology Focus: Drilling and Completion Fluids (November 2021)

2021 ◽  
Vol 73 (11) ◽  
pp. 50-50
Author(s):  
Ergun Kuru
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
New Technologies ◽  
Drilling Fluid ◽  
Oil And Gas Industry ◽  
Drilling Fluids ◽  
Gas Wells ◽  
Essential Components ◽  
Fluid Properties ◽  
Drilling Operations

Design and development of optimal drilling-fluid systems, as well as their proper maintenance while drilling, are essential components of any successful drilling campaign. As the oil and gas industry is drilling in more-challenging areas (e.g., unconventional shale oil/gas wells, deepwater offshore wells, and deep high-pressure/high-temperature sour gas wells), the demand for more-accurate real-time assessment of the downhole state of the drilling fluids during drilling operations increases. Recent developments in drilling systems automation provide a multitude of opportunities to have real-time monitoring of drilling-fluid properties and early diagnosis of drilling-fluid-related complications that might arise while drilling. Coupled with closed-loop control of surface and downhole drilling-fluid properties, automated monitoring of fluid properties would allow rig personnel to make timely corrections to the drilling-fluid program, which eventually would lead to more-cost-efficient and safer drilling operations. This feature provides examples of such new technologies that can be used as part of the automated drilling-fluid monitoring system, allowing real-time control of drilling-fluid rheological properties (i.e., density and viscosity) and management of solids content with potential benefits of real-time management of equivalent circulating density, effective hole cleaning/cuttings transport, increasing drilling rate, and reducing nonproductive time, resulting in safer wells drilled at minimum costs. Recommended additional reading at OnePetro: www.onepetro.org. SPE 199101 - Field Results of a Real-Time Drilling-Fluid Monitoring System by Sérgio Magalhães, Universidade Federal Rural do Rio de Janeiro, et al. SPE 200990 - Intelligent Pressure-Control System for Managed-Pressure Drilling by Zhao Hui Song, Engineering Technology Research Institute of XDEC, et al. SPE 203389 - Real-Time Measurement of Drilling-Fluid Rheology and Density Using Acoustics by Paul Ofoche, Texas A&M University, et al.

Real Time Automation of Cutting Carrying Capacity Index to Predict Hole Cleaning Efficiency and Thereby Improve Well Drilling Performance

2021 ◽  
Author(s):  
Mohammed M Al-Rubaii ◽  
Dhafer Al-Shehri ◽  
Mohamed N Mahmoud ◽  
Saleh M Al-Harbi ◽  
Khaled A Al-Qahtani
Keyword(s):  
Real Time ◽  
Carrying Capacity ◽  
Drilling Fluid ◽  
Model Development ◽  
Cleaning Efficiency ◽  
Time Model ◽  
Well Drilling ◽  
Hole Cleaning ◽  
Drilling Performance ◽  
Drilling Operations

Abstract Hole cleaning efficiency is one of the major factors that affects well drilling performance. Rate of penetration (ROP) is highly dependent on hole cleaning efficiency. Hole cleaning performance can be monitored in real-time in order to make sure drilled cuttings generated are efficiently transported to surface. The objective of this paper to present a real time automated model to obtain hole cleaning efficiency and thus effectively adjust parameters as required to improve drilling performance. The process adopts a modified real time carrying capacity indicator. There are many hole cleaning models, methodologies, chemicals and correlations, but majority of these models do not simulate drilling operations sequences and are not dependent on practicality of drilling operations. The developed real time hole cleaning indicator can ensure continuous monitoring and evaluation of hole cleaning performance during drilling operations. The methodology of real time model development is by selecting offset mechanical drilling parameters and drilling fluid parameters where collected, analyzed, tested and validated to model strong hole cleaning efficiency indicator that can extremely participate and facilitate a position in drilling automations and fourth industry revolution. The automated hole cleaning model is utilizing real time sensors of drilling and validate the strongest relationships among the variables. The study, analysis, test and validation of the relationships will reveal the significant parameters that will contribute massively for model development procedures. The model can be run as well by using the real time sensors readings and their inputs to be fed into the developed automated model. The developed model of real time carrying capacity indicator profile will be shown as function of depth, drilling fluid density, flow rate of mud pump or mud pump output, and other important factors will be illustrated by details. The model has been developed and validated in the field of drilling operations to empower the drilling teams for better and understandable monitoring and evaluation of hole cleaning efficiency while performing drilling operations. The real time model can provide a vision for better control of mud additives and that will contribute to mud cost effectiveness. The automated model of hole cleaning efficiency optimized the rate of penetration (ROP) by 50% in well drilling performance as a noticeable and valuable improvement. This optimum improvement saved cost and time of rig and drilling of wells and contributed to accelerate wells’ delivery. The innovative real time model was developed to optimize drilling and operations efficiency by using the surface rig sensors and interpret the downhole measurements and that can lead innovatively to other important hole cleaning indicators and other tactics for better development of downhole measurements models that can participate for optimized drilling efficiency.

Developing a Managed Pressure Drilling Strategy for Casing Drilling Operations

2009 ◽  
Vol 62-64 ◽  
pp. 456-465
Author(s):  
Babs Mufutau Oyeneyin ◽  
V.C. Kelessidis ◽  
G. Bandelis ◽  
P. Dalamarinis
Keyword(s):  
Real Time ◽  
Drilling Fluid ◽  
Fluid Flows ◽  
Well Design ◽  
Managed Pressure Drilling ◽  
Wellbore Pressure ◽  
Theoretical Predictions ◽  
Drilling Operations ◽  
Horizontal Annuli ◽  
Casing Drilling

Casing drilling can be an effective method of reducing drilling costs and minimising drilling problems but its uptake around the world has been slow with only a few wells drilled so far with casing. Complex geological features like the high overburden on top of shallow unconsolidated reservoirs characteristic of offshore West Africa can benefit from casing drilling when effectively combined with Managed Pressure Drilling technique. For the industry to develop a managed pressure drilling capability that will allow today’s generation of complex wells to be drilled safely with casing, it is necessary to develop models that include the effect of eccentricity , rotation and fluid rheology at bottom hole conditions on flow and pressure regimes, and to embed these models within an easy to use, intuitive well design package for pre planning and as a real time tool to monitor and provide forward simulations based on real time rig and downhole data. The paper presents new results of the theoretical predictions of the wellbore pressure regimes incurred when different types of drilling fluid flows in concentric and eccentric horizontal annuli. The concentric and eccentric casing drilling results are compared with parallel predictions from conventional drillstring results from developed analytical solutions integrated into the VisWELL(DeskTop Simulator) , which is used in simulating well operations.

scholarly journals Assessing the Relation between Mud Components and Rheology for Loss Circulation Prevention Using Polymeric Gels: A Machine Learning Approach

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1377
Author(s):  
Musaab I. Magzoub ◽  
Raj Kiran ◽  
Saeed Salehi ◽  
Ibnelwaleed A. Hussein ◽  
Mustafa S. Nasser
Keyword(s):  
Machine Learning ◽  
Rheological Properties ◽  
Nearest Neighbor ◽  
Drilling Fluid ◽  
Gradient Boosting ◽  
K Nearest Neighbor ◽  
Wide Range ◽  
Machine Learning Approach ◽  
Drilling Operations

The traditional way to mitigate loss circulation in drilling operations is to use preventative and curative materials. However, it is difficult to quantify the amount of materials from every possible combination to produce customized rheological properties. In this study, machine learning (ML) is used to develop a framework to identify material composition for loss circulation applications based on the desired rheological characteristics. The relation between the rheological properties and the mud components for polyacrylamide/polyethyleneimine (PAM/PEI)-based mud is assessed experimentally. Four different ML algorithms were implemented to model the rheological data for various mud components at different concentrations and testing conditions. These four algorithms include (a) k-Nearest Neighbor, (b) Random Forest, (c) Gradient Boosting, and (d) AdaBoosting. The Gradient Boosting model showed the highest accuracy (91 and 74% for plastic and apparent viscosity, respectively), which can be further used for hydraulic calculations. Overall, the experimental study presented in this paper, together with the proposed ML-based framework, adds valuable information to the design of PAM/PEI-based mud. The ML models allowed a wide range of rheology assessments for various drilling fluid formulations with a mean accuracy of up to 91%. The case study has shown that with the appropriate combination of materials, reasonable rheological properties could be achieved to prevent loss circulation by managing the equivalent circulating density (ECD).

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

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3592
Author(s):  
Naipeng Liu ◽  
Di Zhang ◽  
Hui Gao ◽  
Yule Hu ◽  
Longchen Duan
Keyword(s):  
Decision Making ◽  
Rheological Properties ◽  
Real Time ◽  
Drilling Fluid ◽  
Time Measurement ◽  
Decision Making Process ◽  
Rate Of Penetration ◽  
The Real ◽  
Real Time Measurement ◽  
Fluid Properties

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.

Turbodrill Bottomhole Assembly and Real-Time Data Improve Through-Tubing Coiled-Tubing Drilling Operations in Sour Gas Well

2015 ◽  
Author(s):  
A. Ebrahimi ◽  
P. J. Schermer ◽  
W. Jelinek ◽  
D. Pommier ◽  
S. Pfeil ◽  
...  
Keyword(s):  
Real Time ◽  
Time Data ◽  
Gas Well ◽  
Coiled Tubing ◽  
Real Time Data ◽  
Drilling Operations ◽  
Sour Gas

A Generalized Model for the Field Assessment of Drilling Fluid Viscoelasticity

2021 ◽  
Author(s):  
Chen Hongbo ◽  
Okesanya Temi ◽  
Kuru Ergun ◽  
Heath Garett ◽  
Hadley Dylan
Keyword(s):  
Drilling Fluid ◽  
Field Testing ◽  
Field Application ◽  
Testing Equipment ◽  
Drilling Fluids ◽  
Rotational Viscometer ◽  
Generalized Model ◽  
Fluid Elasticity ◽  
Field Samples ◽  
Drilling Operations

Abstract Recent studies highlight the significant role of drilling fluid elasticity in particle suspension and hole cleaning during drilling operations. Traditional methods to quantify fluid elasticity require the use of advanced rheometers not suitable for field application. The main objectives of the study were to develop a generalized model for determining viscoelasticity of a drilling fluid using standard field-testing equipment, investigate the factors influencing drilling fluid viscoelasticity in the field, and provide an understanding of the viscoelasticity concept. Over 80 fluid formulations used in this study included field samples of oil-based drilling fluids as well as laboratory samples formulated with bentonite and other polymers such as partially-hydrolyzed polyacrylamide, synthesized xanthan gum, and polyacrylic acid. Detailed rheological characterizations of these fluids used a funnel viscometer and a rotational viscometer. Elastic properties of the drilling fluids (quantified in terms of the energy required to cause an irreversible deformation in the fluid's structure) were obtained from oscillatory tests conducted using a cone-and-plate type rheometer. Using an empirical approach, a non-iterative model for quantifying elasticity correlated test results from a funnel viscometer and a rotational viscometer. The generalized model was able to predict the elasticity of drilling fluids with a mean absolute error of 5.75%. In addition, the model offers practical versatility by requiring only standard drilling fluid testing equipment to predict viscoelasticity. Experimental results showed that non-aqueous fluid (NAF) viscoelasticity is inversely proportional to the oil-water ratio and the presence of clay greatly debilitates the elasticity of the samples while enhancing their viscosity. The work efforts present a model for estimating drilling fluid elasticity using standard drilling fluid field-testing equipment. Furthermore, a revised approach helps to describe the viscoelastic property of a fluid that involves quantifying the amount of energy required to irreversibly deform a unit volume of viscoelastic fluid. The methodology, combined with the explanation of the viscoelasticity concept, provides a practical tool for optimizing drilling operations based on the viscoelasticity of drilling fluids.

Machine-Learning for the Prediction of Lost Circulation Events - Time Series Analysis and Model Evaluation

2021 ◽  
Author(s):  
Arturo Magana-Mora ◽  
Mohammad AlJubran ◽  
Jothibasu Ramasamy ◽  
Mohammed AlBassam ◽  
Chinthaka Gooneratne ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Real Time ◽  
Large Scale ◽  
Model Comparison ◽  
Drilling Fluid ◽  
Sensor Data ◽  
False Alarms ◽  
Suitable Model ◽  
Lost Circulation

Abstract Objective/Scope. Lost circulation events (LCEs) are among the top causes for drilling nonproductive time (NPT). The presence of natural fractures and vugular formations causes loss of drilling fluid circulation. Drilling depleted zones with incorrect mud weights can also lead to drilling induced losses. LCEs can also develop into additional drilling hazards, such as stuck pipe incidents, kicks, and blowouts. An LCE is traditionally diagnosed only when there is a reduction in mud volume in mud pits in the case of moderate losses or reduction of mud column in the annulus in total losses. Using machine learning (ML) for predicting the presence of a loss zone and the estimation of fracture parameters ahead is very beneficial as it can immediately alert the drilling crew in order for them to take the required actions to mitigate or cure LCEs. Methods, Procedures, Process. Although different computational methods have been proposed for the prediction of LCEs, there is a need to further improve the models and reduce the number of false alarms. Robust and generalizable ML models require a sufficiently large amount of data that captures the different parameters and scenarios representing an LCE. For this, we derived a framework that automatically searches through historical data, locates LCEs, and extracts the surface drilling and rheology parameters surrounding such events. Results, Observations, and Conclusions. We derived different ML models utilizing various algorithms and evaluated them using the data-split technique at the level of wells to find the most suitable model for the prediction of an LCE. From the model comparison, random forest classifier achieved the best results and successfully predicted LCEs before they occurred. The developed LCE model is designed to be implemented in the real-time drilling portal as an aid to the drilling engineers and the rig crew to minimize or avoid NPT. Novel/Additive Information. The main contribution of this study is the analysis of real-time surface drilling parameters and sensor data to predict an LCE from a statistically representative number of wells. The large-scale analysis of several wells that appropriately describe the different conditions before an LCE is critical for avoiding model undertraining or lack of model generalization. Finally, we formulated the prediction of LCEs as a time-series problem and considered parameter trends to accurately determine the early signs of LCEs.

Hole-Cleaning Fibers Improve Cuttings-Carrying Capacity

2021 ◽  
Vol 73 (05) ◽  
pp. 63-64
Author(s):  
Chris Carpenter
Keyword(s):  
Flow Regime ◽  
Carrying Capacity ◽  
Rheological Model ◽  
Drilling Fluid ◽  
Test Method ◽  
Abu Dhabi ◽  
Rheological Parameters ◽  
Cleaning Efficiency ◽  
Hole Cleaning ◽  
Drilling Operations

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 203147, “Investigating Hole-Cleaning Fibers’ Mechanism To Improve Cutting Carrying Capacity and Comparing Their Effectiveness With Common Polymeric Pills,” by Mohammad Saeed Karimi Rad, Mojtaba Kalhor Mohammadi, SPE, and Kourosh Tahmasbi Nowtarki, International Drilling Fluids, prepared for the 2020 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, held virtually 9–12 November. The paper has not been peer reviewed. Hole cleaning in deviated wells is more challenging than in vertical wells because of the boycott effect or the eccentricity of the drillpipe. Poor hole cleaning can result in problems such as borehole packoff or excessive equivalent circulating density. The complete paper investigates a specialized fibrous material (Fiber 1) for hole-cleaning characteristics. The primary goal is to identify significant mechanisms of hole-cleaning fibers and their merits compared with polymeric high-viscosity pills. Hole-Cleaning Indices Based on a review of the literature, most effective parameters regarding hole cleaning in different well types were investigated. These parameters can be classified into the following five categories: - Well design (e.g., hole angle, drillpipe eccentricity, well trajectory) - Drilling-fluid properties (e.g., gel strength, mud weight) - Formation properties (e.g., lithology, cutting specific gravity, cuttings size and shape) - Hydraulic optimizations (e.g., flow regime, nozzle size, number of nozzles) - Drilling practices (e.g., drillpipe rotation speed, wellbore tortuosity, bit type, rate of penetration, pump rate) In this research, rheological parameters and parameters of the Herschel-Bulkley rheological model are considered to be optimization inputs to increase hole-cleaning efficiency of commonly used pills in drilling operations. The complete paper offers a detailed discussion of both the importance of flow regime and the role of the Herschel-Bulkley rheological model in reaching a better prognosis of drilling-fluid behavior at low shear rates. The properties of the fibrous hole-cleaning agent used in the complete paper are provided in Table 1. Test Method Two series of tests were performed. The medium of the first series is drilling water, with the goal of evaluating the efficiency of Fiber 1 in fresh pills. The second series of tests was per-formed with a simple polymeric mud as a medium common in drilling operations. Formulations and rheological properties of both test series are provided in Tables 4 and 5 of the complete paper, respectively.

Development of Digital Twins for Drilling Fluids: Local Velocities for Hole Cleaning and Rheology Monitoring

2021 ◽  
Author(s):  
Mehrdad Gharib Shirangi ◽  
Roger Aragall ◽  
Reza Ettehadi ◽  
Roland May ◽  
Edward Furlong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rheological Properties ◽  
Drilling Fluid ◽  
Training Data ◽  
Drilling Fluids ◽  
Hole Cleaning ◽  
Digital Twins ◽  
Wide Range ◽  
Drilling Operations ◽  
Cuttings Bed

Abstract In this work, we present our advances to develop and apply digital twins for drilling fluids and associated wellbore phenomena during drilling operations. A drilling fluid digital twin is a series of interconnected models that incorporate the learning from the past historical data in a wide range of operational settings to determine the fluids properties in realtime operations. From several drilling fluid functionalities and operational parameters, we describe advancements to improve hole cleaning predictions and high-pressure high-temperature (HPHT) rheological properties monitoring. In the hole cleaning application, we consider the Clark and Bickham (1994) approach which requires the prediction of the local fluid velocity above the cuttings bed as a function of operating conditions. We develop accurate computational fluid dynamics (CFD) models to capture the effects of rotation, eccentricity and bed height on local fluid velocities above cuttings bed. We then run 55,000 CFD simulations for a wide range of operational settings to generate training data for machine learning. For rheology monitoring, thousands of lab experiment records are collected as training data for machine learning. In this case, the HPHT rheological properties are determined based on rheological measurement in the American Petroleum Institute (API) condition together with the fluid type and composition data. We compare the results of application of several machine learning algorithms to represent CFD simulations (for hole cleaning application) and lab experiments (for monitoring HPHT rheological properties). Rotating cross-validation method is applied to ensure accurate and robust results. In both cases, models from the Gradient Boosting and the Artificial Neural Network algorithms provided the highest accuracy (about 0.95 in terms of R-squared) for test datasets. With developments presented in this paper, the hole cleaning calculations can be performed more accurately in real-time, and the HPHT rheological properties of drilling fluids can be estimated at the rigsite before performing the lab experiments. These contributions advance digital transformation of drilling operations.

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