Academic literature on the topic 'HPHT rheology model'

Summary Research currently has shown two contradicting conclusions about silica nanoparticle (SNP) application in mud fluid. While different studies have concluded that adding SNPs reduces the rheological properties, others have found that this is not the case. Therefore, this work was carried out to add to the literature and research that has already been done by different scholars on the performance of SNPs in water-based muds (WBMs). The synthesis of SNPs was performed by the sol-gel process based on the Stöber method in a mixture of a catalyst ammonium hydroxide containing tetraethyl orthosilicate (TEOS), ethanol, and water. The distribution of particle dispersion, size, and zeta potential of SNP analysis was obtained using the dynamic light scattering analysis. Rheological analysis indicated good rheology at different temperatures with 0.5 wt% and 1.0 wt% silica concentration. Furthermore, viscosity and yield point (YP) were stabilized with nanoparticles (NPs at elevated temperatures (up to 176°C) as well as the reference mud maintained rheology (up to 121°C) and above that temperature, there was a drastic change indicating failure. Aging at temperatures above 121°C for 16 hours showed that NP WBMs remained stable with minor changes in rheology. Using bigger sized SNPs than previously used resulted to enhancement in the rheology of WBMs. Previous studies had used SNPs in sizes of 20–40 nm which negatively affected mud rheology. In this study, SNP of a bigger size resulted in rheological property enhancement. It is believed that particle size with other dynamics and mechanisms that still need to be investigated, for example, zeta potential, repulsive and attractive forces are some of the factors in play that affect nanoparticle performance in mud fluids. The obtained rheological data for different NP muds were matched to the traditional drilling mud rheological models to ascertain the best fit model that would be applied to an efficient design and the data fitted the Herschel-Bulkley model. Filtration tests at high pressure and high temperature (HPHT) conditions also indicated that synthesized SNPs used in the mud fluid resulted in a slightly low permeable thin mudcake and a low API gravity filtration loss which have great advantages when drilling through highly permeable formations. Filtrate loss was reduced by 7.5, 9.1, 15.4, and 6.7% when temperature increased to 100, 121, 149, and 176 °C at 1 wt% silica concentration, respectively. The mudcake was also improved and thickness reduced by 30 and 25% at 0.5 wt% silica concentration when temperatures increased from 149 and 176°C, respectively, compared to the reference mud (R) under HPHT conditions. The research results provide a comprehensive evaluation of an enhanced WBM using SNPs for HPHT applications. The investigated NP has the potential to improve drilling mud properties which may led to less formation damage and efficient drilling operations.

Nanoparticle drilling fluids have the potential to minimize wellbore instability problems. There is a need for the development of a new generation of drilling fluids to handle wellbore instability problems especially in lost circulation zones and shale problems. A rheological model is needed to accurately describe the rheology of nano base drilling fluid because limited experimental data are currently available for using carbon nano particles in water base mud and Inability of current methods to address shale strength with increased open hole time reduction of critical mud weight to prevent hole collapse in challenging environment like depleted formation, narrow margin, HPHT wells. The study attempted to produce nano particles locally where the in-situ formation of floating catalyst was used. It involved the floating of catalyst particles by thermal decomposition of organometallic where argon, acethelene and nitrogen gas were used. The CNP WBM formulation was compared with the conventional base mud. Result Show that the CNP mud gave a better result for the fluid loss which can be used as a fluid loss property in the study mud, CNP gave 6.2mls while soltex gave 8.2mls and also CNP gave a thin and firm filter cake of 0.18cm as compared to the soltex formulated mud. Therefore, CNP is a good fluid loss additive and enhances the rheological properties of mud, also helped to improve and add value to local content in the oil and gas industry and save us the cost through the application of locally sourced drilling fluid and save foreign exchange and It has export potentials in the Gulf of Guinea and other oil provinces.

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 real-time operations. Our specific focus is prediction of cuttings bed thickness along the wellbore in hole cleaning and prediction of high-pressure high-temperature (HPHT) rheological properties (in downhole conditions). In both applications, we present procedures to develop accurate digital twins for prediction of drilling fluid properties in real-time drilling operations. In the hole cleaning application, 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 (14.7 psi and 150 F) 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) 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 R2) for test datasets. With developments presented in this paper, the hole cleaning calculations can be performed in real-time, and the HPHT rheological properties of drilling fluids can be estimated at the rig site avoiding the need to wait for the laboratory experimental results.

Abstract High-temperature rheology testing is critical for all fracturing applications to design the well-specific breaker and additive schedule. The rheology depends on the source water quality, testing temperatures, shear profiles, and additives-stabilizers-breaker combinations used for the test. The process for each treatment requires extensive staff to fine-tune the optimal fluid formulations requiring proportional laboratory resources and time. Data analytics intelligent system design can be implemented beyond analytical mathematical correlations to reduce the time and resource requirements. A total of 820 rheology tests were conducted using the high-pressure/high-temperature (HP/HT) rheometer Chandler 5550 and ISO 13503-1 guideline. Temperatures ranged from 200 to 336°F and fluid systems consisted of borate and metallic crosslinkers. A structured database with 40 input-output features was prepared to digitize each rheology curve by incorporating the source water parameters, laboratory setup details, additive concentrations, and rheology (consistency and behavior indices) results. ML algorithms and techniques were then applied to the database to predict the rheology for given testing parameters. The algorithm inputs were prepared as the source water quality (i.e., monovalent/divalent ions, minerals, salinity, hardness etc.) and the test temperature. The outputs predicted were set to be the detailed fluid formulation for specified viscosity and fluid stability requirements. Data cleaning and ingestion were done thoroughly to remove nonphysical outliers such as bob-climbing during testing. A detailed parametric correlation study followed and revealed the impact of different parameters, especially divalent ions such as Ca+2 and Mg+2, and total dissolved solids on the rheology. The training set to holdout set ratio was fixed at 90:10 for different trials. Further, 5-fold cross validation was used to choose the hyperparameters for the final model. To predict fluid formulation/target rheology in terms of additive concentrations, which is a continuous quantity, regression-based models were attempted. Ridge regression and ensemble methods such as random forest and boosting type models were trained. Boosting-based models gave an average 88% goodness of fit (R2) for the holdout datasets. For field implementation, the model results were used to create a digital laboratory request for the laboratory technician instead of having the fracturing design engineer manually handle this task. The physics-based data-driven ML model reduced an average HP/HT runs/well from 20 to 5 yielding a 400% laboratory resource savings. This ML-based workflow is unique and does not exist in the literature. It can enable resource optimization for all large-scale fracturing projects and reduce manual laborious input for generating laboratory requests followed by trial-and-error optimizations with a potential of saving thousands of hours and reduce all the laboratory equipment maintenance costs. The technique can easily be extended to designing cementing fluids, drilling muds, and corrosion properties.

Abstract This paper discusses both technical and project management aspects of drilling fluids services for deepwater and high pressure high temperature (HPHT) offshore drilling projects. The technical discussion part includes deepwater and HPHT specific fluids related concerns such as logistics, narrow drilling window, shallow hazards, gas hydrates, HPHT conditions and low temperature rheology; together with practical solutions for each of them. As some of these challenges cannot be met by only fluids itself, technologies such as managed pressure drilling (MPD), dual-gradient drilling (DGD) and use of special downhole tools are also included in the discussions. The project management aspect is covered for both the planning and execution phases. A newly developed Four Stage Planning Guideline (4SPG) with a recommended timetable is proposed for high-profile offshore drilling projects. Starting from fluids selection to preparation of the contingency plans is discussed in detail for the planning phase. Execution phase is discussed mainly for service company representatives on how to follow main or contingency plans effectively and ensure good communication is achieved with all parties involved. Work model presented in this paper can be used as a complete guideline by operating and service company representatives in order to increase the success rate of these high-risk offshore drilling projects and ensure learnings are captured in a structured way for continuous improvement.

Abstract Flat rheology drilling fluids are synthetic-based fluids designed to provide better drilling performance with flat rheological properties for deep water and/or cold environments. The detailed mud properties are mainly measured in laboratories and are often measured twice a day in the field. This prevents real-time mud performance optimization and negatively affects the decisions. If the real-time estimation of mud properties, which affects decision-making in time, is absent, the ROP may slow down, and serious drilling problems and severe economic losses may take place. Consequently, it is important to evaluate the mud properties while drilling to capture the dynamics of mudflow. Unlike other mud properties, mud density (MD) and Marsh funnel viscosity (MFV) are frequently measured every 15–20 minutes in the field. The objective of this work is to predict the rheological properties of the flat rheology drilling fluids in real-time using machine learning (ML). A proposed approach is followed to firstly predict the viscometer readings at 300 and 600 RPM (R600 and R300) and then calculate the other mud properties using the existing equations in the literature. For forecasting the viscometer readings, the created model using the decision trees (DT) demonstrated good accuracy. The results revealed a maximum average absolute percentage error (AAPE) below 4.5% and a correlation coefficient (R) of greater than 0.97. The estimated rheological properties showed a good matching with the actual values with low errors. Introduction Drilling fluid or mud is a mixture of a base fluid and additional ingredients in certain proportions used while drilling. Several materials are added to adjust the mud properties such as, but are not limited to, the weighting agents for density, the fluid loss control materials, and viscosifiers for controlling the rheological properties (e.g., plastic viscosity (PV), yield point (YP), and gel strength). Despite mud represents 5% to 15% of total drilling costs, it may cause most of drilling problems. Drilling fluids are put to even greater strain by high-angle wells, high temperatures, and lengthy horizontal sections across pay zones (Bloys et al., 1994). Newtonian and non-Newtonian are the main two types of fluids. Newtonian fluid is characterized by a constant viscosity at a certain temperature and pressure. Non-Newtonian fluid such as most drilling fluids and cement slurries has viscosities that rely on shear rates for certain pressure and temperature (Rabia, 2002). Drilling fluids are mainly classified as water-based mud (WBM) or oil-based mud (OBM). OBM typically contains a base oil representing the external continuous phase; a saline aqueous solution representing the internal phase, emulsifiers at the interface, and other additives for suspension, weighting materials, oil-wetting, fluid loss, and rheology control additives. Oil based drilling fluids have two main categories which are invert-emulsion and all-oil drilling fluids (Alsabaa et al., 2020). An invert emulsion mud contains about 50:50 to about 95:5 by volume oil to water ratio. An all-oil mud contains 100% oil; that is, there is no aqueous internal phase. The invert emulsion mud is used to tackle some drilling problems like shale instability, minimize damage to water zones, and, and protect the casing and tubing against corrosion (Gray and Grioni, 1969; Growcock et al., 1994). The invert emulsion mud is characterized by its low toxicity and the brine is added to control the salinity to prevent water molecules from invading the formations (Hossain and Al-Majed, 2015). The invert emulsion drilling fluid is mainly used to drill the HPHT wells owing to its thermal stability which outperforms the WBM and can be used in drilling up to 400 ℉ (Lee et al., 2012).

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.

Abstract Super critical carbon dioxide (SC-CO2) flooding is one of the most important enhanced oil recovery (EOR) methods used for conformance control and deep diversion of SC-CO2. It minimizes gravity override of SC-CO2 bypassing oil in the lower part of the formation. This paper investigates the impact of various parameters such as liquid/liquid ratio, different foam qualities and different injection modes on the SC-CO2-foam quality and its rheological properties. SC-CO2 foam can control the SC-CO2 mobility, enhance the sweep efficiency in reservoirs and improve the conformance control. Experimental results shows that combining foam with supercritical and dense CO2 will reduce the mobility of SC-CO2 to oil and water, stabilize the SC-CO2 injection front and mitigate the gravity override to a great extent resulting in less amount of unwept oil and better displacement efficiency and more recovery gain. Different set of lab experiments designed and conducted to identify the right ratio that can drastically increase SC-CO2 viscosity. In this work, we explored the rheological properties of SC-CO2 foam/gel chemicals with different pressure and temperature. Two different types of surfactants were tested. The experimental setup and conditions were designed to allow surfactant to mix with SC-CO2 under high reservoir pressure and temperature (HPHT) to create foam to evaluate and screen the foam quality and texture. The rheological properties of the SC-CO2-foam were investigated by varying the shear rate, shear stress, foam quality, injection modes and foaming agent concentrations at reservoir conditions. The effects of foam quality and liquid/liquid ratio, pressure and temperature on SC-CO2-foam at synthetic brine-environment rheology behavior, stability and mobility of foam were investigated. The foam study experiments were conducted using different scenarios: once by injecting SC-CO2 and surfactant solutions simultaneously and another time by alternate injection of CO2/surfactant solution at different flow rates at different foam qualities. The experimental results have shown that the foam mobility (total mobility of CO2 /surfactant solution) decreased with increasing foam quality ranging from 20% to 80%. The rheological properties of N2-foam were investigated and compared with SC-CO2-foam properties. This was correlated with the images of the high pressure, high temperature (HPHT) foams that were captured through microscope at different time intervals and analyzed to indicate their stability.

Abstract The analysis of wellbore stability in gas wells is vital for effective drilling operations, especially in Brown fields and for modern drilling technologies. Tensile failure mode of Wellbore stability problems usually occur when drilling through hydrocarbon formations such as shale, unconsolidated sandstone, sand units, natural fractured formations and HPHT formations with narrow safety mud window. These problems can significantly affect drilling time, costs and the whole drilling operations. In the case of the candidate onshore gas well Niger Delta, there was severe lost circulation events and gas cut mud while drilling. However, there was need for a consistent adjustment of the tight drilling margin, flow, and mud rheology to allow for effective filter-cake formation around the penetrated natural fractures and traversed depleted intervals without jeopardizing the well integrity. Several assumptions were validly made for formations with voids or natural fractures, because the presence of these geological features influenced rock anisotropic properties, wellbore stress concentration and failure behavior with end point of partial – to-total loss circulation events. This was a complicated phenomenon, because the pre-drilled stress distribution simulation around the candidate wellbore was investigated to be affected by factors such as rock properties, far-field principal stresses, wellbore trajectory, formation pore pressure, reservoir and drilling fluids properties and time without much interest on traversing through voids or naturally fractured layers. This study reviews the major causes of the severe losses encountered, the adopted fractured permeability mid-line mudweight window mitigation process, stress caging strategies and other operational decisions adopted to further salvage and drill through the naturally fractured and depleted intervals, hence regaining the well integrity by reducing NPT and promoting well-early-time-production for the onshore gas well Niger Delta.