Prediction of Foam Rheology Models Parameters Utilizing Machine Learning Tools.

Rheological models are usually used to predict foamed fluid viscosity; however, obtaining the model constants under various conditions is challenging. Hence, this paper investigated the effect of different variables on foam rheology, such as shear rate, temperature, pressure, surfactant types, gas phase, and salinity, using a high-pressure high-temperature foam rheometer. Power-law, Bingham plastic, and Casson fluid models fit the experimental data well. Therefore, the data were fed to different machine learning techniques to evaluate the rheological model constants with different features. In this study, seven different machine learning techniques have been applied to predict the rheological models' constants, including decision tree, random forest, XGBoost (XGB), adaptive gradient boosting, gradient boosting, support vector regression, and voting regression. We evaluated the performance of our machine learning models using the coefficient of determination (R2), cross-plots, root-mean-square error, and average absolute percentage error. Based on the prediction outcomes, the XGB model outperformed the other ML models. The XGB model exhibited remarkably low error rates, achieving a prediction accuracy of 95% under ideal conditions. Furthermore, our prediction results demonstrated that the Casson model accurately captured the rheological behavior of the foam. Additionally, we used Pearson's correlation coefficients to assess the significance of various properties in relation to the constants within the rheological models. It is evident that the XGB model makes predictions with nearly all features contributing significantly, while other machine learning techniques rely more heavily on specific features over others. The proposed methodology can minimize the experimental cost of measuring rheological parameters and serves as a quick assessment tool.

Temperature, pressure, and duration impacts on the optimal stiffening of carbonates aged in diammonium phosphate solution.

Diammonium phosphate (DAP) has been proven effective in improving the stiffness of weak or acid-damaged carbonates, thereby preserving hydraulic fracture conductivity. The reaction between DAP and calcite in chalk formations primarily produces hydroxyapatite (HAP), which is stiffer than calcite. However, the optimal reaction outcomes vary greatly with factors such as DAP concentration and reaction conditions. This study investigated the DAP-calcite reaction duration, pressure, and temperature effects on the stiffness magnitude of soft Austin chalk. Also, the catalyst effect and depth of HAP formation were examined. The study involved the assessment of stiffness non-destructively (impulse hammering), mineralogy (XRD, SEM), and elemental composition (XRF). The study tested 15 different DAP-chalk reaction variations, where the pressure, temperature, aging time and catalyst addition were modified in each case. The samples' elastic stiffness distributions were then collected and compared to the pre-reaction ones. The results showed that the elastic stiffness increased in all treated samples, with an 181% maximum increase achieved after 72 h at 6.9 MPa and 75 °C. However, the pressure effect was minor compared to the temperature. The SEM images revealed different HAP morphology corresponding to different treatment conditions. Although the treated samples showed an increased intensity of phosphorus throughout the entire sample, the near-surface zone (4-6 mm) was the most affected, as inferred from the XRF elemental analysis. The study's findings can help optimize hydraulic fracturing operations in weak carbonate reservoirs, improving production rates and overall well performance.

In-situ micro-CT scanning and compressive strength assessment of diammonium hydrogen phosphate (DAP) treated chalk.

The occurrence of wellbore mechanical failure is a consequence of the interaction among factors such as in situ stress, rock strength, and engineering procedures. The process of hydrocarbons production, causing reduction of pore pressure, alters the effective stresses in the vicinity of a borehole, leading to borehole instability issues. Estimating the rocks' elastic modulus and compressive strength is essential to comprehend the rock matrix's mechanical response during drilling and production operations. This study aimed to assess the practicality of Diammonium Hydrogen Phosphate (DAP) application as a chemical for strengthening chalk in hydrocarbon reservoirs, to make it resistant to high stresses and failure during drilling and production. The mechanical and physical properties of Austin chalk rock samples treated with DAP under mimicked reservoir conditions were studied. The results showed that DAP is a highly effective carbonate rock consolidating agent that improves the mechanical strength of the chalk. Compressive test measurements conducted on rocks treated at two different temperatures (ambient and 50 °C) showed that DAP effectively strengthened the rock matrix, resulting in an increase in its compressive strength (22-24%) and elastic modulus (up to 115%) compared to the untreated sample. The favorable outcomes of this research suggest that the DAP solution holds promise as a consolidation agent in hydrocarbon reservoirs. This contributes to the advancement of knowledge regarding effective strategies for mitigating mechanical failures of the wellbore during drilling and production.

Mineralogy Impact on Acid Fracture Design in Naturally Fractured Carbonates.

The impacts of a natural fracture on the propagation of a hydraulic fracture has been extensively studied in the past decade. However, there were only minor attempts to understand the impact of natural fractures on acid distribution during acid fracturing. This study provides an understanding of the impact of natural fractures on acid fracturing designs in limestone and dolomite formations. A built-in-house model that solves fracture propagation, acid transport and dissolution, heat transfer, and reservoir productivity was utilized. A sensitivity analysis was conducted to investigate the impact of temperature, injection rate, and acid type. One of the significant differences between limestone and dolomite is the dissolution profile in the hydraulic fracture. The maximum dissolution happens close to the wellbore in limestone, but far from the wellbore in dolomite at low acid temperatures. Also, the hydraulic fracture in dolomite could have lower conductivity (i.e., permeability) compared to the intersecting natural fractures, which is not usually the case in limestone. Hence, the productivity enhancement by acid fracturing in limestone formation is higher than in dolomite. In general, natural fractures reduce productivity, and their negative impact is more significant in dolomite. The sensitivity study showed that the acid temperature significantly impacts fractured well productivity of dolomite, especially at high reservoir permeability where higher dissolution is desired. It is observed that the optimum injection rate that maximizes productivity is much higher in limestone than in dolomite. Also, acid retardation does not positively impact the productivity of dolomite compared to limestone; however, high viscosity might be needed to keep the hydraulic fracture open. This is the first study to reveal mineralogy's impact on acid fracture performance in naturally fractured carbonates.

Thermally Activated Nitrogen/Heat Generating Reaction: A Kinetic Study.

Sodium nitrite and ammonium chloride are the most widely used thermochemicals in the oil and gas industry. The kinetics of this reaction when activated with acids or acid precursors were the subject of extensive research by several researchers. The activation of such a highly spontaneous/vigorous reaction by heat is considered a promising reaction control. In this work, a kinetic study was carried out for the reaction of sodium nitrite and ammonium chloride salt activated by heat at 1-5 M and temperatures of 50-90 °C. The study was carried out in both closed and open systems, with the monitoring of gas evolution and generated pressure. The study showed a relatively higher order for ammonium chloride than sodium nitrite. The excess amount of ammonium chloride, a weak acid, enhanced the reaction as it could be catalyzed by both heat and acid. The obtained kinetics of the nitrogen generating reaction is given as dc/dt = -7.66 × 1011 C o 2.45 e (-91.44 kJ/mol)/RT . The reaction kinetics in this study differs from what is reported in the literature regarding the order of NH4Cl, which was reported to be higher than that of NaNO2 under examined conditions. This study has practical significance for controlling the reactivity of the NH4Cl/NaNO2 nitrogen/heat generating system and calculating/optimizing nitrogen generation for a specific field application.

Effect of Seawater Ions on Polymer Hydration in the Presence of a Chelating Agent: Application to Hydraulic Fracturing.

Seawater (SW) and produced water (PW) could replace freshwater in hydraulic fracturing operations, but their high salinity impacts the fluid stability and results in formation damage. Few researchers investigated SW and PW individual ions' impact on polymer hydration and rheology. This research examines the rheology of carboxy methyl hydroxy propyl guar (CMHPG) polymer hydrated in salt ions in the presence of a chelating agent. The effect of various molar concentrations of SW and PW salt ions on the rheology of CMHPG polymer solution was examined. The tested salt ions included calcium chloride, magnesium chloride, sodium chloride, and sodium sulfate, which were compared to SW and deionized water (DI) solutions. The solutions were tested at 70 °C temperature, 500 psi pressure, and 100 1/s shear rate. A GLDA chelating agent was utilized at different concentrations to examine their impact on stabilizing the solution viscosity. We found that adding the GLDA to magnesium and calcium chloride solutions increased the viscosity. Results showed that sulfate ions control the rheology of seawater due to their similar rheological response to the addition of GLDA. The results help to understand how the SW and PW ions impact the rheology of fracturing fluids.

Machine Learning-Based Accelerated Approaches to Infer Breakdown Pressure of Several Unconventional Rock Types.

Unconventional oil and gas reservoirs are usually classified by extremely low porosity and permeability values. The most economical way to produce hydrocarbons from such reservoirs is by creating artificially induced channels. To effectively design hydraulic fracturing jobs, accurate values of rock breakdown pressure are needed. Conducting hydraulic fracturing experiments in the laboratory is a very expensive and time-consuming process. Therefore, in this study, different machine learning (ML) models were efficiently utilized to predict the breakdown pressure of tight rocks. In the first part of the study, to measure the breakdown pressures, a comprehensive hydraulic fracturing experimental study was conducted on various rock specimens. A total of 130 experiments were conducted on different rock types such as shales, sandstone, tight carbonates, and synthetic samples. Rock mechanical properties such as Young's modulus (E), Poisson's ratio (ν), unconfined compressive strength, and indirect tensile strength (σt) were measured before conducting hydraulic fracturing tests. ML models were used to correlate the breakdown pressure of the rock as a function of fracturing experimental conditions and rock properties. In the ML model, we considered experimental conditions, including the injection rate, overburden pressures, and fracturing fluid viscosity, and rock properties including Young's modulus (E), Poisson's ratio (ν), UCS, and indirect tensile strength (σt), porosity, permeability, and bulk density. ML models include artificial neural networks (ANNs), random forests, decision trees, and the K-nearest neighbor. During training of ML models, the model hyperparameters were optimized by the grid-search optimization approach. With the optimal setting of the ML models, the breakdown pressure of the unconventional formation was predicted with an accuracy of 95%. The accuracy of all ML techniques was quite similar; however, an explicit empirical correlation from the ANN technique is proposed. The empirical correlation is the function of all input features and can be used as a standalone package in any software. The proposed methodology to predict the breakdown pressure of unconventional rocks can minimize the laboratory experimental cost of measuring fracture parameters and can be used as a quick assessment tool to evaluate the development prospect of unconventional tight rocks.

Review of Kerogen's Geomechanical Properties: Experiments and Molecular Simulation.

Measuring the mechanical properties of kerogen, the predominant constituent of organic matter in shale is exceedingly difficult as it constitutes small-scale aggregates interspersed in rocks. Kerogen is characterized by significantly lower stiffness compared to inorganic minerals, thereby the kerogen regions are potential areas for study during, for example, drilling or macroscopic fracture propagation in the course of hydraulic fracturing. For instance, the elastic modulus of kerogen-rich spots is around 10 GPa, while it is about 70 GPa for quartz. Failure of the kerogen nanocantilever beam shows an elastic strain-hardening behavior, indicating a higher energy requirement to propagate a crack. Studies illustrated that the kerogen's mechanical properties are controlled by maceral composition and are positively correlated to the maturity level. This paper provides a comprehensive review of how the mechanical properties of kerogen are elucidated experimentally and contrast the results with the properties delineated from molecular simulation. In addition, we relate kerogen innate attributes, such as maturity and type, to the physical qualities measured and substantiate why accurate knowledge of the mechanical characteristics is pivotal from a hydraulic fracturing perspective.

Rheological Study of Seawater-Based Fracturing Fluid Containing Polymer, Crosslinker, and Chelating Agent.

Freshwater is usually used in hydraulic fracturing as it is less damaging to the formation and is compatible with the chemical additives. In recent years, seawater has been the subject of extensive research to reduce freshwater consumption. The study aims to optimize the rheology of seawater-based fracturing fluid with chemical additives that reduce the formation damage. The studied formulation consists of a polymer, a crosslinker, and a chelating agent to reduce seawater hardness. We used a standard industry rheometer to perform the rheology tests. By comparing five distinct grades [hydroxypropyl guar (HPG) and carboxymethyl hydroxypropyl guar (CMHPG)], we selected the guar derivative with the best rheological performance in seawater. Five different polymers (0.6 wt %) were hydrated with seawater and freshwater to select the suitable one. Then, the best performing polymer was chosen to be tested with (1.6, 4, and 8 wt %) N, N-dicarboxymethyl glutamic acid GLDA chelating agent and 1 wt % zirconium crosslinker. In the first part, the testing parameters were 120 °C temperature, 500 psi pressure, and 100 1/s shear rate. Then, the same formulations were tested at a ramped temperature between 25 and 120 °C. We observed that higher and more stable viscosity levels can be achieved by adding the GLDA after polymer hydration. In seawater, an instantaneous crosslinking occurs once the crosslinker is added even at room temperature, while in freshwater, the crosslinker is activated by ramping the temperature. We noted that, in the presence of a crosslinker, small changes in the chelating agent concentration have a considerable impact on the fluid rheology, as demonstrated in ramped temperature results. It is observed that the viscosities are higher and more persistent at lower concentrations of GLDA than at higher concentrations. The study shows the rheological response when different chemical additives are mixed in saline water for hydraulic fracturing applications.

The Synergetic Impact of Anionic, Cationic, and Neutral Polymers on VES Rheology at High-Temperature Environment.

Hydraulic fracturing operations target enhancing the productivity of tight formations through viscous fluid injection to break down the formation and transport proppant. Crosslinked polymers are usually used for desired viscoelasticity of the fracturing fluid; however, viscoelastic surfactants (VES) became a possible replacement due to their less damaging impact. To design a fracturing fluid with exceptional rheological and thermal stability, we investigated mixing zwitterionic VES with carboxymethyl cellulose (CMC), hydroxyethylcellulose (HEC), or a poly diallyl dimethylammonium chloride (DADMAC) polymers. As a base fluid, calcium chloride (CaCl2) solution was prepared with either distilled water or seawater before adding a polymer and the VES. A Chandler high-pressure, high-temperature (HPHT) viscometer was used to conduct the viscosity measurements at a shear rate of 100 1/s. It has been found that adding 1% CMC polymer to 9% (v/v) VES increases the viscosity more compared to 10% (v/v) VES at reservoir temperatures of 143.3 °C. On the other hand, adding only 1.0% of HEC to 9% (v/v) VES doubled the viscosity and proved more effective than adding CMC. HEC, nevertheless, reduced the system stability at high temperatures (i.e., 148.9 °C). Adding DADMAC polymer (DP) to VES increased the system viscosity and maintained high stability at high temperatures despite being exposed to saltwater. CaCl2 concentration was also shown to affect rheology at different temperatures. The improved viscosity through the newly designed polymer can reduce chemical costs (i.e., reducing VES load), making it more efficient in hydraulic fracturing operations.

Study of the Mechanical Behavior of Organic Matters Contained in Source Rocks: New Insights into the Role of Bitumen.

Assessment of mechanical properties of organic matters contained in unconventional formations is needed to understand the geomechanics of source rocks. The organic matters are part of the source rock matrix, and they are made of kerogen and bitumen. Although the literature has some studies addressing the properties of kerogen and bitumen, no apparent attempts were made to address the mechanical behavior of organic matters as a combination of both. Isolation of organic matters from the rocks for experimental assessments has some risks of altering the original properties because of their delicate nature and their existence as micro- and nanoconstituents. Some computational approaches such as molecular simulation can serve as an alternative platform for the purpose of delineating organic matter properties including the mechanical ones. This work implements available 3D molecular modeling of kerogen and bitumen with different ratios to mimic organic matters that can be investigated for the mechanical properties. Upon the recreation of different configurations of organic matters molecularly, mechanical parameters such Young's, bulk, and shear constants, as well as the stress-strain relationship for the elastic and plastic deformations were extracted. The mechanical behavior was closely monitored before and after saturation with a number of gases that are commonly found in subsurface formations such as methane, carbon dioxide, and nitrogen. The results revealed that the organic matters had a mechanical behavior envelope similar to what were reported for organic-based materials such as polymers. Moreover, the structures containing bitumen exhibited larger values of Poisson's ratio, indicating less likelihood of them to degrade upon applied stresses. The presented data substantiate the importance of accounting for both bitumen and kerogen in modeling the petrophysics and the mechanical behavior of the organic matters.

Development of Oil and Gas Stimulation Fluids Based on Polymers and Recycled Produced Water.

Freshwater scarcity is a highly pressing and accelerating issue facing our planet. Therefore, there is a great incentive to develop sustainable solutions by reusing wastewater or produced water (PW), especially in places where it is generated abundantly. PW represents the water produced as a by-product during oil and gas extraction operations in the petroleum industry. It is the largest wastewater stream within the industry, with hundreds of millions of produced water barrels per day worldwide. This research investigates a reuse opportunity for PW to replace freshwater utilization in well stimulation applications. Introducing an environmentally friendly chelating agent (GLDA) allowed formulating a PW-based fluid system that has similar rheological properties in fresh water. This work aims at evaluating the rheological properties of the developed stimulation fluid. The thickening profile of the fluid was controlled by chelation chemistry and varying different design parameters. The experiments were carried out using a high-pressure, high-temperature (HPHT) viscometer. Variables such as polymer concentration and pH have a great impact on the viscosity, while temperature and concentration of the chelating agents are shown to control the thickening profile, as well as its stability and breakage behaviors. Furthermore, 50 pptg of carboxymethyl hydroxypropyl guar (CMHPG) polymer in 20 wt.% chelating solution was shown to sustain 172 cP viscosity for nearly 2.5 h at 150 °F and 100 S-1 shear rate. The newly developed fluid system, solely based on polymer, chelating agent, and PW, showed great rheological capabilities to replace the conventional stimulation fluids based on fresh water. The newly developed fluid can also have economic value realization due to fewer additives, compared with conventional fluids.

Machine Learning-Based Propped Fracture Conductivity Correlations of Several Shale Formations.

In hydraulic fracturing operations, small rounded particles called proppants are mixed and injected with fracture fluids into the targeted formation. The proppant particles hold the fracture open against formation closure stresses, providing a conduit for the reservoir fluid flow. The fracture's capacity to transport fluids is called fracture conductivity and is the product of proppant permeability and fracture width. Prediction of the propped fracture conductivity is essential for fracture design optimization. Several theoretical and few empirical models have been developed in the literature to estimate fracture conductivity, but these models either suffer from complexity, making them impractical, or accuracy due to data limitations. In this research, and for the first time, a machine learning approach was used to generate simple and accurate propped fracture conductivity correlations in unconventional gas shale formations. Around 350 consistent data points were collected from experiments on several important shale formations, namely, Marcellus, Barnett, Fayetteville, and Eagle Ford. Several machine learning models were utilized in this research, such as artificial neural network (ANN), fuzzy logic, and functional network. The ANN model provided the highest accuracy in fracture conductivity estimation with R 2 of 0.89 and 0.93 for training and testing data sets, respectively. We observed that a higher accuracy could be achieved by creating a correlation specific for each shale formation individually. Easily obtained input parameters were used to predict the fracture conductivity, namely, fracture orientation, closure stress, proppant mesh size, proppant load, static Young's modulus, static Poisson's ratio, and brittleness index. Exploratory data analysis showed that the features above are important where the closure stress is the most significant.

Reduction of Breakdown Pressure by Filter Cake Removal Using Thermochemical Fluids and Solvents: Experimental and Numerical Studies.

The process of well cleanup involves the removal of an impermeable layer of filter cake from the face of the formation. The inefficient removal of the filter cake imposes difficulty on fracturing operations. Filter cake's impermeable features increase the required pressure to fracture the formation. In this study, a novel method is introduced to reduce the required breakdown pressure to fracture the formation containing the water-based drilling fluid filter cake. The breakdown pressure was tested for five samples of similar properties using different solutions. A simulated borehole was drilled in the core samples. An impermeable filter cake using barite-weighted drilling fluid was built on the face of the drilled hole of each sample. The breakdown pressure for the virgin sample without damage (filter cake) was 6.9 MPa. The breakdown pressure increased to 26.7 MPa after the formation of an impermeable filter cake. Partial removal of filter cake by chelating agent reduced the breakdown pressure to 17.9 MPa. Complete dissolution of the filter cake with chelating agents resulted in the breakdown pressure approximately equivalent to the virgin rock breakdown pressure, i.e., 6.8 MPa. The combined thermochemical and chelating agent solution removed the filter cake and reduced the breakdown pressure to 3.8 MPa. Post-treatment analysis was carried out using nuclear magnetic resonance (NMR) and scratch test. NMR showed the pore size redistributions with good communication between different pores after the thermochemical removal of filter cake. At the same time, there was no communication between the different pores due to permeability impairment after filter cake formation. The diffusion coupling through NMR scans confirmed the higher interconnectivity between different pores systems after the combined thermochemical and chelating agent treatment. Compressive strength was measured from the scratch test, confirming that filter cake formation caused added strength to the rock that impacts the rock breakdown pressure. The average compressive strength of the original specimen was 44.5 MPa that increased to 73.5 MPa after the formation of filter cake. When the filter cake was partially removed, the strength was reduced to 61.7 MPa. Complete removal with chelating agents removed the extra strength that was added due to the filter cake presence. Thermochemical and chelating agents resulted in a significantly lower compressive strength of 25.3 MPa. A numerical model was created to observe the reduction in breakdown pressure due to the thermochemical treatment of the filter cake. The result presented in this study showed the engineering applications of thermochemical treatment for filter cake removal.

Chelating Agents Usage in Optimization of Fracturing Fluid Rheology Prepared from Seawater.

Hydraulic fracturing consumes massive volumes of freshwater that is usually scarce and costly. Such operation is not sustainable, and hence seawater could be used as an alternative. Nevertheless, seawater has high total dissolved solids (TDS), affecting the fracturing fluid rheology and providing a damage potential to the subterranean hydrocarbon reservoirs. Resolving these issues requires fracturing fluid systems with many additives, which results in an expensive and non-eco-friendly system. This study proposes eco-friendly and biodegradable chelating agents that could replace many additives such as scale inhibitors and crosslinkers. The study aims to optimize the rheology of seawater fracturing fluids using a chelating agent and polymer. By optimizing N,N-Dicarboxymethyl glutamic acid (GLDA) conditions, high viscosity was achieved using a standard industry rheometer. The GLDA was mixed with carboxymethyl hydroxypropyl guar (CMHPG) polymer and tested in both deionized water (DW) and seawater (SW). The polymer was examined first, where the rheology did not show a time-dependent behavior. The polymer in SW showed a slightly higher viscosity than in DW. The GLDA and CMHPG were tested at different temperatures, pH, and concentrations. These sets showed a time-dependent viscosity behavior, which can be utilized in various fracturing steps. Results showed that the solution pH and GLDA concentration significantly impacted the fluid viscosity magnitude and behavior. The developed formulation is shear thinning, where the viscosity declines as the shear rate increases. The temperature negatively impacted the viscosity and caused the formulation to break. The study provided an understanding of how to optimize the rheology of SW fracturing fluid based on GLDA chelating and CMHPG polymer.

An Artificial Intelligence-Based Model for Performance Prediction of Acid Fracturing in Naturally Fractured Reservoirs.

Acid fracturing is one of the most effective techniques for improving the productivity of naturally fractured carbonate reservoirs. Natural fractures (NFs) significantly affect the design and performance of acid fracturing treatments. However, few models have considered the impact of NFs on acid fracturing treatments. This study presents a simple and computationally efficient model for evaluating acid fracturing efficiency in naturally fractured reservoirs using artificial intelligence-based techniques. In this work, the productivity enhancement due to acid fracturing is determined by considering the complex interactions between natural and hydraulic fractures. Several artificial intelligence (AI) techniques were examined to develop a reliable predictive model. An artificial neural network (ANN), a fuzzy logic (FL) system, and a support vector machine (SVM) were used. The developed model predicts the productivity improvement based on reservoir permeability and geomechanical properties (e.g., Young's modulus and closure stress), natural fracture properties, and design conditions (i.e., acid injection rate, acid concentration, treatment volume, and acid types). Also, several evaluation indices were used to evaluate the model reliability including the correlation coefficient, average absolute percentage error, and average absolute deviation. The AI model was trained and tested using more than 3100 scenarios for different reservoir and treatment conditions. The developed ANN model can predict the productivity improvement with a 3.13% average absolute error and a 0.98 correlation coefficient, for the testing (unseen) data sets. Moreover, an empirical equation was extracted from the optimized ANN model to provide a direct estimation for productivity improvement based on the reservoir and treatment design parameters. The extracted equation was evaluated using validation data where a 4.54% average absolute error and a 0.99 correlation coefficient were achieved. The obtained results and degree of accuracy show the high reliability of the proposed model. Compared to the conventional simulators, the developed model reduces the time required for predicting the productivity improvement by more than 60-fold; therefore, it can be used on the fly to select the best design scenarios for naturally fractured formations.

Mass and Heat Transfer of Thermochemical Fluids in a Fractured Porous Medium.

The desire to improve hydraulic fracture complexity has encouraged the use of thermochemical additives with fracturing fluids. These chemicals generate tremendous heat and pressure pulses upon reaction. This study developed a model of thermochemical fluids' advection-reactive transport in hydraulic fractures to better understand thermochemical fluids' penetration length and heat propagation distance along the fracture and into the surrounding porous media. These results will help optimize the design of this type of treatment. The model consists of an integrated wellbore, fracture, and reservoir mass and heat transfer models. The wellbore model estimated the fracture fluid temperature at the subsurface injection interval. The integrated model showed that in most cases the thermochemical fluids were consumed within a short distance from the wellbore. However, the heat of reaction propagated a much deeper distance along the hydraulic fracture. In most scenarios, the thermochemical fluids were consumed within 15 ft from the fracture inlet. Among other design parameters, the thermochemical fluid concentration is the most significant in controlling the penetration length, temperature, and pressure response. The model showed that a temperature increase from 280 to 600 °F is possible by increasing the thermochemical concentration. Additionally, acid can be used to trigger the reaction but results in a shorter penetration length and higher temperature response.

Data-Driven Acid Fracture Conductivity Correlations Honoring Different Mineralogy and Etching Patterns.

Acid-fracturing operations are mainly applied in tight carbonate formations to create a highly conductive path. Estimating the conductivity of a hydraulic fracture is essential for predicting the fractured well productivity. Several models were developed previously to estimate the conductivity of acid-fractured rocks. In this research, machine learning methods were applied to 560 acid fracture experimental datapoints to develop several conductivity correlations that honor the rock types and etching patterns. Developing one universal correlation often results in significant error. To develop conductivity correlations, various data preprocessing methods were applied to remove the outliers and failed experiments. Features that did not contribute to precise predictions were removed through regularization. A machine learning classifier was built to predict the etching pattern based on the input data. We generated a multivariate linear regression model and compared it with other models generated through ridge regression. In addition to that, artificial neural network-based model was proposed to predict the fracture conductivity of several carbonate rocks such as dolomite, chalk, and limestone. The performance of the developed models was assessed using well-known metrics such as precision, accuracy, mean squared error, recall, and correlation coefficients. Cross-validation was also employed to assure accuracy and avoid overfitting. The classifier accuracy was 93%, while the regression model resulted in a relatively high correlation coefficient.

A Novel Approach to Improve Acid Diversion in Carbonate Rocks Using Thermochemical Fluids: Experimental and Numerical Study.

The distribution of acid over all layers of interest is a critical measure of matrix acidizing efficiency. Chemical and mechanical techniques have been widely adapted for enhancing acid diversion. However, it was demonstrated that these often impact the formation with damage after the acid job is completed. This study introduces, for the first time, a novel solution to improve acid diversion using thermochemical fluids. This method involves generating nitrogen gas at the downhole condition, where the generated gas will contribute in diverting the injected acids into low-permeability formations. In this work, both lab-scale numerical and field-scale analytical models were developed to evaluate the performance of the proposed technique. In addition, experimental measurements were carried out in order to demonstrate the application of thermochemical in improving the acid diversion. The results showed that a thermochemical approach has an effective performance in diverting the injected acids into low-permeability rocks. After treatment, continuous wormholes were generated in the high-permeability rocks as well as in low-permeability rocks. The lab-scale model was able to replicate the wormholing impact observed in the lab. In addition, alternating injection of thermochemical and acid fluids reduced the acid volume 3.6 times compared to the single stage of thermochemical injection. Finally, sensitivity analysis indicates that the formation porosity and permeability have major impacts on the acidizing treatment, while the formations pressures have minor effect on the diversion performance.

Impact of Diversion on Acid Fracturing of Laminated Carbonate Formations: A Modeling Perspective.

Acid-fracturing treatment schedules usually contain diversion stages either to reduce fluid loss or equally stimulate pay zones. Near-wellbore diversion employs solid or mechanical diverters to direct acid to less-stimulated zones. This study focuses on the impact of near-wellbore diversion on the acid fracturing of calcite/dolomite laminated formations. The study was performed using an integrated acid fracture and productivity model. Simulations showed that when acid preferentially etches more reactive lamina, channels with infinite conductivity can be achieved. Nevertheless, deploying diverters to equally stimulate pay zones can result in less conductive but equally stimulated zones. The productivity model showed that the preferential etching that naturally occurs when injecting acid results in better well performance, if a continuous pay zone is assumed. This study also shows that the optimum acid fracture design conditions in laminated formations are similar to those of calcite-dominated formations.