Cement is an inorganic binding material and has evolved over 5,000 years from natural materials to industrial production with changes in the chemical compositions and the particle size distributions. Natural cement made from limestone containing clay minerals continued to be used until the nineteenth century. When water is added to the cement, the cement will react with the water and bind with many types of inorganic materials to form durable composites for various types of applications. Historically, cement is the most valuable material developed by humans to enhance growth and developments around the world. Initially, cement was used to build Egyptian pyramids. Around 300 BC, Romans also used it with various types of admixtures to construct many types of buildings. The main focus over centuries has been on developing strong and durable cement. Currently, cement is being used in multiple applications in both onshore and offshore constructions. There are now standards set for various types of Portland cements and oil well cements to ensure quality of production and also help with the multiple cement applications. Cement is the largest manufactured material around the world, and in recent years, over 4 trillion tons are being manufactured annually.
There have been many concrete bridges, highways, dams, buildings, storage facilities, foundations and pipes that have failed over the past hundred years due to loadings, earthquakes, fires and aging. In the year 2018, there was bridge failure in Italy with about 40 deaths and many injuries. Failures can result in many types of losses and impact the economy, and because of this, there is a need for real-time monitoring of the changing conditions in the infrastructures.
Well cement is used under different conditions of exposures compared to the cement used in the conventional construction industry. Recent case studies on cementing failures have clearly identified several issues that resulted in various types of delays in the cementing operations. The catastrophic accident in the Gulf of Mexico in April 2010 is one of the world’s worst oil spills. Real-time monitoring of the changes in the cement in-situ during the installation and service life of the wells are critical to evaluate the performance of the cemented wells.
In recent years, highly sensing smart cement with real-time monitoring tools has been developed to monitor cement performance in various types of infrastructures including oil wells and all concrete structures.
Smart cement (sensing cement)
Based on the applications, cement (Portland cements, oil well cements) is mixed with less than 0.1% conductive filler (carbon fibers, basaltic fiber or a mixture with diameter in the micrometer range) to make it a chemo-thermo-piezoresistive material (U.S. Pattern 10,481,143). Smart cement can sense any changes going on from the time of mixing through the entire service life. It can also sense the changes in the water–to-ratio, additives, temperature and any pressure applied to the cement in terms of piezoresistivity strain. In addition, recent studies have demonstrated that smart cement can detect gas leaks and earthquakes. The failure compressive strain for the smart cement was 0.2% at peak compressive stress and the resistivity change is of the order of two hundred percent (200%), making it over 1000 times (100,000%) more sensitive.
Based on experience and sensitive material property that can be monitored in the field, electrical resistivity was selected. Digital resistivity meter was used to measure the resistivity of the cement slurries and semi-solids. The measurable resistivity range for the instrument was 0.01Ω-m to 400 Ω-m.
The electrical resistance was measured using an inductance, capacitance, and resistance (LCR) meter during the cement curing and compression, tension and pressure testing in the laboratory and field. To minimize the contact resistances, the resistance was measured at 300 kHz using the two-probe method.
The experimental set-up using the LCR meter with the two probes to measure the electrical Impedance with Frequency (a) Photo of the LCR Device connected to the specimen and (b) Schematic of the test configuration with the mold.
Results and Analyses
During curing, the electrical properties of the smart cement varied with the curing conditions in the laboratory and field. New quantification models have been developed to characterize the sensing of the smart cement during curing and during loading to quantify the piezoresisitive behavior in the laboratory and field.
Curing of cement
Initial resistivity was measured immediately after mixing the smart cement. Initial resistivity of the smart cement was 1.05 Ωm (Table 1). During the curing process under room condition (relative humidity of 50% and temperature of 72oF, the resistivity rapidly changed with time. As as result, there are several parameters that can be used in monitoring the curing (hardening process) of the cement. The parameters are the initial resistivity (ρo), minimum resistivity (ρmin), time to reach the minimum resistivity (tmin) and resistivity after 24 hours of curing (ρ24). After initial mixing, the electrical resistivity reduced to a minimum value (ρmin), and then it gradually increased with time. Time to reach minimum resistivity, tmin, can be used as an index of speed of chemical reactions and cement setting time. With solidification, the resistivity increased sharply with curing time. The increase in electrical resistivity was caused by the formation of large amounts of hydration products in the cement matrix, and the resistivity continuously increased with the curing time.
Table 1: Electrical Resistivity Model parameters for smart cement for 28 days of curing.
|Cement Type||Initial Resistivity, ρo (Ω-m)||ρmin (Ω-m)||t min (min)||to (min)||R2||p1||q1|
|Smart Cement||1.05 + 0.03||0.96 + 0.01||80 + 5.0||110||0.99||0.61||0.38|
Vipulanandan p-q curing model
Based on experimental results, a theoretical model was developed to predict the electrical resistivity of smart cement with curing time. The Vipulanandan p-q curing model is defined as follows:
Where r is the electrical resistivity (Ω-m); t is the curing time (minutes); rmin: minimum electrical resistivity (Ω-m); tmin: time corresponding minimum electrical resistivity (rmin), p1 to and q1(t) are model parameters. In general, model parameters are influenced by the composition of the cement and curing conditions (temperature, humidity and stress). The parameter t0 is influenced by the initial resistivity r0.
28 Days of Curing
The resistivity after 28 days of curing was 14.54 Ω.m, more than 1285% increase compared to the initial resistivity. The resistivity after 28 days of curing was over 320% higher compared to the resistivity after 24 hours (1 day). This cleary indicates the sensitivity of resistivity to the cement curing.
Vipulanandan Model parameters p1 and q1 were 0.61 and 0.38. This model also predicted the curing trend very well (Figure 1). The coefficient of determination (R2) was 0.99 and the RMSE (root mean square error) was 0.21 Ω.m.
Figure 1. Electrical Resistivity of Smart Cement in the Laboratory During 28 Days of Curing.
It is important to characterize the sensing property and resistivity changes in the smart cement under stress. The piezoresisitive responses (stress-resistivity strain relationship) for 28 days cured smart cement is shown in Fig. 5. The piezoresistivity of the smart cement at failure after 28 day of curing was 252% as shown in Fig. 5. The smart cement piezoresistive response was over 1250 times (125,000%) higher compared to the compressive failure strain of cement of 0.2%, which was used in the past for monitoring. This also clearly indicates the sensitivity of smart cement for stress monitoring in the cement.
Vipulanandan p-q Piezoresistivity Model
The Vipulanandan p-q piezoresistivity model was used to predict the observed trends for the smart cement and represented as follows:
Where s is the stress (psi or MPa); sf: is the compressive strength (MPa); percentage of change in resistivity strain due to the stress; percentage of change in ρ at failure and ∆r is the change in the ρ. The initial electrical resistivity (ro) (at s = 0 MPa) and p2 and q2 are material parameter of the constitutive model.
28 Days of Curing
It is important to quantify the piezoresisitive behavior of the smart cement. The specimens were cured under room condition and the stress- piezoresistive strain response was non-linear (Figure 2).
Vipulanandan Model parameters p2 and q2 were 0.108 and 0.57. This model predicted piezoresistive behavior very well (Figure 2). The R2 was 0.99 and the RMSE was 0.24 MPa.
Figure 2. Piezoresistive Behavior of Smart Cement after 28 days of Curing.
After reviewing a few potential test sites, Energy Research Park (ERP) at the University of Houston in Houston, Texas was selected to install the field well. Many factors including geology, swelling and soft clays, changing surrounding conditions (weather, ground water and active zone in the ground), environmental regulations and accessibility to the site for long-term monitoring had to be considered in selecting the test site since the focus of the study was to demonstrate the sensitivity of the smart cemented field well. The selected site had swelling clays with fluctuating moisture conditions (active zone) which represent nearly the worst conditions that could be encountered when installing oil wells. The top 20 feet of the soil was swelling clay soil with liquid limit of over 50% and was characterized as CH soil. The active zone in the Houston area is about 15 feet, indicating relatively large moisture fluctuation in the soil causing it to swell and shrink. The water table was 20 feet below ground and the soil below the water table was CL clay.
Figure 3 Schematic View of the Field Well with the Instrumentation
It has been shown that the two probes with AC current can be used to determine the electrical resistance changes in the smart cement and drilling fluid. It was also important use other standard tools for measuring the changes in the cement sheath and compare it to the resistance changes. For practical reasons, no instrument was placed on the casing, and a totally independent system was developed to be placed in the cement sheath. The probes were placed at various vertical depths at 15 levels. Eight probes were also placed horizontally at each level. Additionally, eighteen stain gages and nine thermocouples were included in the instrumentation (Figure 3)
Externally Instrumented Casing
Installation of the Field Well
A commercial company familiar with drilling and cementing wells in an urban setting was selected to install the field well. A very large drilling truck with 14 in diameter drill was used to drill the hole and place the 95/8 in diameter standard steel casing. The total length of the casing was 42 feet and needed pieces (including well head and needed connections to lift the casing) were welded together to make a single unit. Initial 15 feet was drilled without any drilling fluid. Polymer based drilling fluid was used to drill the rest of the borehole. After completing the drilling, the casing and the instrumentation units were centered and lowered into the borehole (Figure 4). Monitoring of the resistance between the probes, temperature and strains (strain gages) were measured.
To simulate a pressure test, air pressure (Pi) was applied inside the 0.8 in diameter tube to the entire depth of 40 feet (Figure 3) to verify the piezoresistivity of the cement-sheath. Initially, the electrical resistances (Ro in Ohms) were measured along the entire depth before applying the pressure. This test was done regularly to demonstrate the sensitivity of the smart cement to the applied small pressures of up to 80 psi (0.55 MPa).
Instrumented Casing Being Airlifted for Installation in the borehole
Figure 4. Installation of the Field Well with the Instrumentation
Monitoring of Resistance, Strain and Temperature
The smart cement was mixed in the field and used for cementing the field well. It is important to identify the measurable parameters in the cement sheath and also determine the changes with time and depth. In the field. normally fiber optics are used for monitoring, and it depends on the changes in the strain in the cement sheath. The strain in the cement will be influenced by the cement curing, stress and temperature in the cement sheath. Over the past 4.5 years (over 1600 days), thousands of data has been collected on the monitoring parameters. It is important to quantify the changes in the measuring parameters with important variable such as the depth. In order to investigate the changes with depth, top level (CH soil), middle level (above the water table, CH soil ) and the bottom level (below the water table, CL soil) were selected for investigation.
Resistance (R): The top level was about 1 ft. below the ground surface. The initial resistivity of the smart cement measured using the two probes was 1.03 Ω.m comparable to the laboratory mixed cement of 1.05 Ω.m. The resistance in the top level changed from 22 Ω to 221 Ω, about 9.05 times (905%) change in the resistance (Figure 5). The changes in the cement sheath resistance were not uniform but overall showed continuous increase. The rapid increase in the cement resistance was due to environmental temperature lowering and moisture loss in the cement. The rapid decrease in the cement resistance was due to environmental temperature increase and cement saturation due to flooding.
Temperature (T): The temperature continuously fluctuated with time with no clear trend. Over the 4.5 years, the minimum and maximum measured temperature in the cement sheath was 68oF and 97.2oF, a maximum change of 42.8%. The average temperature at the top level was about 77.7oF, a 14% decrease from initial temperature of 90.3oF which would have been influenced by cement hydration.
Strain (S): The strain gage resistance increased from 123 Ω to 133 Ω during the period of 4.5 years with some fluctuations (Figure 5). The change in strain gage resistance was about 8.1%. The tensile strain at the top level was about 3.3×10-6.
Based on the measured monitoring parameters in the cement sheath, change in electrical resistance showed the largest change compared to the changes in temperature and strain. Therefore, it is important to develop models to predict this change with time for monitoring the well.
Figure 5. Electrical Resistance, Strain and Temperature variation in top level During the 4.5 years.
Comparing Resistance Change
From the measurements made at all levels, clearly the electrical resistance change was the highest. Therefore, it is of interest compare the changes and trends in the electrical resistance with the depth. The electrical resistance change was not uniform in the top and middle levels in the field well. The electrical resistance changed by 905% in the top level close to the surface. The top level also showed the largest fluctuation in the resistance changes based on the weather patterns. Both the environmental temperature and rainfall influenced the fluctuation in the resistance at the top level (Figure 6). The electrical resistance changed by 590% at middle level (15 feet below the ground) with much less in fluctuation compare to the top level. The electrical resistance change at the bottom level, below the water table, was 272% (Figure 6). The difference in the electrical resistance changes were also due to difference in cement curing conditions in the field ground. The top level was exposed to outside temperature and had air curing, while the middle level was under moisture curing and bottom level was cured under water.
Figure 6. Electrical Resistance data for top, middle and bottom levels in field well for 4.5 years.
Prediction of Electrical Resistivity of Smart Cement
The value of initial resistivity of smart cement was 1.03 Ω.m. immediately after mixing. The electrical resistivity of smart cement was 10.4 Ω.m. after 4.5 years of curing (Figure 7). The time for minimum resistivity was 195 minutes after mixing (Table 2). Vipulanandan Curing Model parameters p1 and q1 were 0.76 and 0.24 respectively after 4.5 years of curing. Also, the RMSE for curing model was 0.86 Ω.m and R2 was 0.97.
Figure 7. Comparing the Prediction of Electrical Resistivity at the Top Level Using the AI Model and Vipulanandan Curing Model up to 4.5 years.
Table 2. Electrical resistivity model parameters for smart cement in field for 4.5 years.
|Level||ρo (Ω.m.)||tmin (min)||to (min)||p1||q1|
It is important to demonstrate the piezoresistivity of the smart cement in the field and the sensitivity of smart cement for small pressure changes. Therefore, the test was performed at 10 psi (0.07 MPa) increments up to 80 psi (0.55 MPa). The maximum value of vertical piezoresistive strain (3D) for smart cement after 4.5 years of curing was 13.5% at an applied pressure of 0.55 MPa (Figure 8). This is a clear demonstration of 3D sensitivity of the smart cement. By measuring the piezoresistive strain in the smart cement, it will also be possible predict the pressure in the casing using the models. The value of model parameters p2 and q2 for piezoresistivity model are 0.025 and 0.417 and the RMSE of 0.02 Ωm with R2 of 0.99.
Figure 8. Vertical Piezoresistive Strain – Pressure Relationship (3D) for smart cement in the field after 4.5 years of curing.
The following conclusions based on this study are advanced:
- Based on the laboratory data and field data, electrical resistivity showed the largest variation compared to strain and temperature changes. Therefore, electrical resistivity was selected as the monitoring parameter for the smart cement.
- Smart cement was highly piezoresistive with the addition of less than 0.1% conductive filler with no nanoparticles which makes it a very cost effective modification. Also, the change in the resistivity was positive for compression and tensile loading and smart cement is a 3- dimensional (3D) sensor.
- Vipulanandan curing model and piezoresistive model predicted smart cement behavior in the laboratory and field very well.
This study was supported by Department of Energy (DOE/NETL/RPSEA), National Science Foundation (NSF-I Corp), the Center for Innovative Grouting Materials and Technology (CIGMAT) and the Texas Hurricane Center for Innovative Technology (THC-IT) at the University of Houston, Texas. Sponsors are not responsible for the entire conclusion made from this study.
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