The rock unconfined compressive strength (UCS) is one of the key parameters for geomechanical and reservoir modeling in the petroleum industry. Obtaining the UCS by conventional methods such as experimental work or empirical correlation from logging data are time consuming and highly cost. To overcome these drawbacks, this paper utilized the help of artificial intelligence (AI) to predict (in a real-time) the rock strength from the drilling parameters using two AI tools. Random forest (RF) based on principal component analysis (PCA), and functional network (FN) techniques were employed to build two UCS prediction models based on the drilling data such as weight on bit (WOB), drill string rotating speed (RS), drilling torque (T), stand-pipe pressure (SPP), mud pumping rate (Q), and the rate of penetration (ROP). The models were built using 2333 data points from well (A) with 70:30 training to testing ratio. The models were validated using unseen dataset (1300 data points) of well (B) which is located in the same field and drilled across the same complex lithology. The results of the PCA-based RF model outperformed the FN in terms of correlation coefficient (R) and average absolute percentage error (AAPE). The overall accuracy for PCA-based RF was R of 0.99 and AAPE of 4.3%, and for FN yielded R of 0.97 and AAPE of 8.5%. The validation results showed that R was 0.99 for RF and 0.96 for FN, while the AAPE was 4% and 7.9% for RF and FN models, respectively. The developed PCA-based RF and FN models provide an accurate UCS estimation in real-time from the drilling data, saving time and cost, and enhancing the well stability by generating UCS log from the rig drilling data.

The evaluation of the quality of unconventional hydrocarbon resources becomes a critical stage toward characterizing these resources, and this evaluation requires the evaluation of the total organic carbon (TOC). Generally, TOC is determined from laboratory experiments; however, it is hard to obtain a continuous profile for the TOC along the drilled formations using these experiments. Another way to evaluate the TOC is through the use of empirical correlation, and the currently available correlations lack the accuracy especially when used in formations other than the ones used to develop these correlations. This study introduces an empirical equation for the evaluation of the TOC in Devonian Duvernay shale from only gamma-ray and spectral gamma-ray logs of uranium, thorium, and potassium as well as a newly developed term that accounts for the TOC from the linear regression analysis. This new correlation was developed based on the artificial neural networks (ANNs) algorithm which was learned on 750 datasets from Well-A. The developed correlation was tested and validated on 226 and 73 datasets from Well-B and Well-C, respectively. The results of this study indicated that for the training data, the TOC was predicted by the ANN with an AAPE of only 8.5%. Using the developed equation, the TOC was predicted with an AAPE of only 11.5% for the testing data. For the validation data, the developed equation overperformed the previous models in estimating the TOC with an AAPE of only 11.9%.

Predicting the rate of penetration (ROP) is challenging especially during horizontal drilling. This is because there are many factors affecting ROP. Machine learning techniques are very promising in identifying the structural relationships existing between the inputs and target variables; these techniques were recently successfully applied to estimate the ROP in different wellbore shapes and through various formation lithologies. This study is aimed to introduce a random forest (RF) regression model for ROP prediction based on many factors such as the drilling mechanical parameters (torque, pipe speed, and weight on bit), hole cleaning parameters (the drilling fluid flowrate and pump pressure), and formation properties (formation bulk density and formation resistivity). In addition to its superiority in providing accurate results, RF has the advantage of providing interpretable rules. These rules help in understanding the relationships between the regressors and the target variable. Actual field measurements collected during horizontally drilling carbonate formation were used for training and testing the RF model. Unseen data collected from another well were used for validating the optimized model. Using the K-fold validation method, the proposed RF model has proven its superior performance when compared to artificial neural networks and support vector regression models. An illustrative example on a sample of real drilling data is presented to explain how the RF regression model is applied to the drilling data. In addition, developing interpretable regression rules through merging RF results is explained. These rules can guide drilling practitioners in accomplishing drilling projects at minimum time and cost.

Pressure–volume–temperature (PVT) properties of crude oil are considered the most important properties in petroleum engineering applications as they are virtually used in every reservoir and production engineering calculation. Determination of these properties in the laboratory is the most accurate way to obtain a representative value, at the same time, it is very expensive. However, in the absence of such facilities, other approaches such as analytical solutions and empirical correlations are used to estimate the PVT properties. This study demonstrates the combined use of two machine learning (ML) technique, viz., functional network (FN) coupled with particle swarm optimization (PSO) in predicting the black oil PVT properties such as bubble point pressure (P b ), oil formation volume factor at Pb, and oil viscosity at Pb. This study also proposes new mathematical models derived from the coupled FN-PSO model to estimate these properties. The use of proposed mathematical models does not need any ML engine for the execution. A total of 760 data points collected from the different sources were preprocessed and utilized to build and train the machine learning models. The data utilized covered a wide range of values that are quite reasonable in petroleum engineering applications. The performances of the developed models were tested against the most used empirical correlations. The results showed that the proposed PVT models outperformed previous models by demonstrating an error of up to 2%. The proposed FN-PSO models were also compared with other ML techniques such as an artificial neural network, support vector regression, and adaptive neuro-fuzzy inference system, and the results showed that proposed FN-PSO models outperformed other ML techniques.

Swelling elastomer seals and packers provide effective zonal isolation in difficult oil and gas fields, resulting in significant savings in rig time and development expenses. Companies involved in petroleum engineering applications provide no information about the longevity or endurance of swelling elastomer packers when exposed to different conditions in the well. This paper describes the design and construction of a test facility for long-term performance assessment of actual full-scale water and oil-swell packers, and the results of the 5-year study. The ten packers are made of three types of swelling elastomers, are kept in crude oil and saline solutions at room and high temperatures, and are exposed to varying high pressures. Regular logs of readings were maintained over the whole study period. Earlier sealing was observed in elastomers immersed in low-salinity water and subjected to high temperature. Rate of swelling was higher in water-based elastomers than in oil-based seals. One packer never sealed completely. Pressurized tubes either retained sealing the whole time or re-sealed after removing of pressure or reducing it to a lower value. Results for the fast-swell and medium-swell elastomer, low- and high-salinity water and crude oil, and low- and high-operating temperatures were mostly in line with short-term tests on laboratory samples published earlier. This unique longevity assessment study can be used by drilling engineers and developers in assessing the suitability of swelling elastomers for target fields and in improving the design of swell packers.

This study aims to better understand the aluminum oxide agglomerates breakup mechanism, consequently determining the best solution for the solid rocket motor (SRM) nozzle erosion problem. Two-phase air-water flow experimental investigation was conducted as a substitute for liquid aluminum agglomerates and exhaust combustion gases. The results show that increasing the exhaust air velocity enhances the droplet's breakup tendency to reduce the average diameter and increase droplet numbers per the testing channel volume. Numerical models were constructed and validated using the experimental results. The percentage error in the droplets’ average diameter and the number is between 6 and 15% and 8 and 18%, respectively. Furthermore, the effect of reducing the liquid surface tension was studied. The results showed that it facilitates water bodies’ separation from the interface surface, because of the reduced bounding forces between surface’s molecules, which enhances the breakup process (0.5–17% increase in the droplets’ average diameter and 4–100% increase in its number) and reduces the droplets impact on the nozzle walls, hence reducing the SRM nozzle erosion problem.

Several correlations are available to determine the fracture pressure, a vital property of a well, which is essential in the design of the drilling operations and preventing problems. Some of these correlations are based on the rock and formation characteristics, and others are based on log data. In this study, five artificial intelligence (AI) techniques predicting fracture pressure were developed and compared with the existing empirical correlations to select the optimal model. Real-time data of surface drilling parameters from one well were obtained using real-time drilling sensors. The five employed methods of AI are functional networks (FN), artificial neural networks (ANN), support vector machine (SVM), radial basis function (RBF), and fuzzy logic (FL). More than 3990 datasets were used to build the five AI models by dividing the data into training and testing sets. A comparison between the results of the five AI techniques and the empirical fracture correlations, such as the Eaton model, Matthews and Kelly model, and Pennebaker model, was also performed. The results reveal that AI techniques outperform the three fracture pressure correlations based on their high accuracy, represented by the low average absolute percentage error (AAPE) and a high coefficient of determination (R 2 ). Compared with empirical models, the AI techniques have the advantage of requiring less data, only surface drilling parameters, which can be conveniently obtained from any well. Additionally, a new fracture pressure correlation was developed based on ANN, which predicts the fracture pressure with high precision (R 2 = 0.99 and AAPE = 0.094%).

A unique hybrid cooling, heating, and power (HCHP) concept has been recently developed as an alternative to environmental control units. It combines a small-scale organic Rankine cycle (ORC) with a vapor compression cycle. The unique drive-train design flexibly and efficiently converts engine waste heat into useful energy in the form of cooling, heating, and power depending upon the energy needs. Compared to a standard military environmental control unit which puts an electric load on a diesel generator, the HCHP system uses engine exhaust heat as the primary energy input. Utilizing the exhaust heat can potentially provide 27% reduction on fuel consumption when operating in the cooling mode. When cooling is not needed, it is able to provide power and/or heating output using engine waste heat—a significant advantage over other heat activated cooling technologies. The prototype unit based on the HCHP design has been developed to demonstrate the concept. It leveraged the microchannel heat exchanger and scroll expander technologies to achieve high-performance, small-size, and low-cost design in order to meet the growing distributed energy applications.

The standard torque and drag (T&D) modeling programs have been extensively used in the oil and gas industry to predict and monitor the T&D forces. In the majority of cases, there has been variability in the accuracy between the pre-calculated (based on a T&D model) and actual T&D values, because of the dependence of the model’s predictability on guessed inputs (matching parameters) which may not be correctly predicted. Therefore, to have a reliable model, program users must alter the model inputs and mainly the friction coefficient to match the actual T&D. This, however, can conceal downhole conditions such as cutting beds, tight holes, and sticking tendencies. The objective of this study is to develop an intelligent machine to predict the continuous profile of the surface drilling torque to enable the detection of operational problems ahead of time. This paper details the development and evaluation of an intelligent system that could promote safer operation and extend the response time limit to prevent undesired events. Actual field data of Well-1, starting from the time of drilling a 5-7/8-in. horizontal section until 1 day prior to the stuck pipe incident, were used to train and test three models: random forest, artificial neural network, and functional network, with an 80/20 training-to-testing data ratio, to predict the surface drilling torque. The independent variables for the model are the drilling surface parameters, namely: flow rate (Q), hook load (HL), rate of penetration (ROP), rotary speed (RS), standpipe pressure (SPP), and weight-on-bit (WOB). The prediction capability of the models was evaluated in terms of correlation of coefficient (R) and average absolute error percentage (AAPE). The model with the highest R and lowest AAPE was selected to continue with the analysis to detect downhole abnormalities. The best-developed model was used to predict the surface drilling torque on the last day leading up to the incident in Well-1, which represents the normal and healthy trend. Then, the model was coupled with a multivariate metric distance called “Mahalanobis” to be used as a classification tool to measure how close an actual observation is to the predictive normal and healthy trend. Based on a pre-determined threshold, each actual observation was labeled “NORMAL” or “ANOMAL.” Well-2 with a stuck pipe incident was used to assess the capability of the developed system in detecting downhole abnormalities. The results showed that in Well-1, where a stuck pipe incident was reported, a continuous alarm was detected by the developed system 9 h before the drilling crew observed any abnormality, while the alarm was detected 7 h prior to any observation by the crew in Well-2. The developed intelligent system could help the drilling crew to detect downhole abnormalities in real-time, react, and take corrective action to mitigate the problem promptly.

This paper describes the measurement of convective heat transfer coefficients and friction factors for sCO 2 flowing in a smooth tube and compares the results with published correlations for validation. The paper also describes the Heat Exchange and Experimental Testing (HEET) rig recently designed and built at the U.S. Department of Energy’s (DoE’s) National Energy Technology Laboratory (NETL) in Morgantown, WV. The Wilson-plot technique used for measuring the heat transfer coefficients is described along with the data reduction process. The Wilson-plot technique was chosen as the basis for the design of NETL’s HEET rig. Advantages of the Wilson-plot technique include the (1) ability to measure high convective heat transfer coefficients accurately, (2) ability to measure average heat transfer coefficient for complicated heat exchange geometries like those produced using additive manufacturing, (3) ability to measure heat transfer coefficients on both sides of a heat exchanger independently, and (4) simplicity of experimental setup. Capabilities of the HEET rig include pressure to 24 MPa (3500 psig), temperature to 538 °C (1000 °F), mass flow rate to 1.5 kg/s (3 lb/s), and Re to 500,000. The rig is designed to operate with pure CO 2 or a mixture of CO 2 and up to 10% N 2 by volume to study the impact of gas mixtures typical of direct-fired sCO 2 power cycles on the convective heat transfer and pressure drop. Preliminary tests in the HEET rig were performed with smooth stainless-steel tube and pure CO 2 , and the results were compared with published correlations for Nusselt number (Nu) and friction factor. Over a Reynolds number (Re) range from 58,000 to 228,000, measured Nu was compared to predictions using the Dittus and Boelter equation (Kreith and Bohn, 1993, “Principles of Heat Transfer, West Publishing Company”) within 5% and measured friction factors were compared to predictions using the McAdams correlation (“McAdams, 1954, “Heat Transmission,” 3rd ed., McGraw Hill, New York)” for smooth tube to be within 5%.

Aluminized propellants are frequently used in solid rocket motors (SRMs) to increase specific impulse. However, as the propellant combusts, the aluminum is oxidized into aluminum oxide (Al 2 O 3 ), it agglomerates into molten droplets that attach to the outside wall of the rocket nozzle. This phenomenon negatively impacts ballistics performance because the droplets remain attached to the inner wall of propulsion chambers. This buildup of particles tends to erode the wall, decreasing the performance and sustainability of the rocket. This study presents both experimental and computational fluid dynamics (CFD) to investigate the relationship between gas velocity and molten particle size for the vertically arrayed combustion chamber. Also, the Weber number and the Froude number are monitored to explain the breakup phenomenon and the condition of alumina flow in the whole testing channel. This study focused mainly on the vertical arrangement of the propulsion chamber with the cold experimental and simulation investigating the role of the liquid water in addition to a comparison with the horizontal chamber case. Unlike the horizontal setup, a greater number of droplets with smaller average droplet diameter present in the vertical setup; however, Froude number follows the same trend as for the horizontal C-D nozzle setup.

This paper presents a solid rocket motor (SRM), both experiment and simulation of alumina molten flow patterns using the cold flow case. The combustion of aluminum composite propellants in SRM chambers causes high-temperature and pressure conditions resulting in the liquid alumina (Al 2 O 3 ) as a combustion product, and it tends to agglomerate into molten droplets, which impinge on the propulsion chamber walls and then flow along the nozzle wall. This liquid alumina in the flow creates problems such as chemical erosion of the propellant and mechanical erosion of the nozzle. Thus, particle size and droplet distribution are considered to affect the erosive behavior. Furthermore, for the rocket motor, converging–diverging (C–D) of the nozzle is used because of its high performance in terms of the rate of change of momentum. In this study, to investigate the relationship between air velocity and molten particle size, the study was mainly focused on the horizontal arrangement of the combustion chamber with the cold flow with the liquid.

Low temperature and high pressure conditions favor the formation of gas clathrate hydrates which is undesirable during oil and gas industries operation. The management of hydrate formation and plugging risk is essential for the flow assurance in the oil and gas production. This study aims to show how hydrate management in the deepwater gas well testing operations in the South China Sea can be optimized. To prevent the plugging of hydrate, three hydrate management strategies are investigated. The first method, injecting thermodynamic hydrate inhibitor (THI) is the most commonly used method to prevent hydrate formation. THI tracking is utilized to obtain the distribution of mono ethylene glycol (MEG) along the pipeline. The optimal dosage of MEG is calculated through further analysis. The second method, hydrate slurry flow technology is applied to the gas well. Pressure drop ratio (PDR) is defined to denote the hydrate blockage risk margin. The third method is the kinetic hydrate inhibitor (KHI) injection. The delayed effect of KHI on the hydrate formation induction time ensures that hydrates do not form in the pipe. This method is effective in reducing the injection amount of inhibitor. The problems of the three hydrate management strategies which should be paid attention to in industrial application are analyzed. This work promotes the understanding of hydrate management strategies and provides guidance for hydrate management optimization in oil and gas industry.

In this paper, pragmatic and robust techniques have been developed to simultaneously interpret absolute permeability and relative permeability together with capillary pressure in a naturally fractured carbonate formation from wireline formation testing (WFT) measurements. By using two sets of pressure and flow rate field data collected by a dual-packer tool, two high-resolution cylindrical near-wellbore numerical models are developed for each dataset on the basis of single- and dual-porosity concepts. Then, simulations and history matchings are performed for both the measured pressure drawdown and buildup profiles, while absolute permeability is determined and relative permeability is interpreted with and without considering capillary pressure. Compared to the experimentally measured relative permeability curves for the same formation collected from the literature, relative permeability interpreted with consideration of capillary pressure has a better match than those without considering capillary pressure. Also, relative permeability obtained from dual-porosity models has similar characteristics to those from single-porosity models especially in the region away from the endpoints, though the computational expenses with dual-porosity models are much larger. Absolute permeabilities in the vertical and the horizontal directions of the upper layer are determined to be 201.0 mD and 86.4 mD, respectively, while those of the lower layer are found to be 342.9 mD and 1.8 mD, respectively. Such a large vertical permeability of the lower layer reflects the contribution of the extensively distributed natural fractures in the vertical direction.

Self-healing wind turbine blades offer a substantial offset for costly blade repairs and failures. We discuss the efforts made to optimize the self-healing properties of wind turbine blades and provide a new system to maximize this offset. Copper wire coated by paraffin wax was embedded into fiber-reinforced polymer (FRP) samples incorporated with Grubbs' first-generation catalyst. The wires were extracted from cured samples to create cavities that were then injected with the healing agent, dicyclopentadiene (DCPD). Upon sample failure, the DCPD and catalyst react to form a thermosetting polymer to heal any crack propagation. Three-point bending flexural tests were performed to obtain the maximum flexural strengths of the FRP samples before and after recovery. Using those results, a hierarchy of various vascular network configurations was derived. To evaluate the healing system's effect in a real-life application, a prototype wind turbine was fabricated and wind tunnel testing was conducted. Using ultraviolet (UV) dye, storage and transport processes of the healing agent were observed. After 24 h of curing time, Raman spectroscopy was performed. The UV dye showed dispersion into the failure zone, and the Raman spectra showed the DCPD was polymerized to polydicyclopentadiene (PDCPD). Both the flexural and wind tunnel test samples were able to heal successfully, proving the validity of the process.

Drillstrings that include one or more axial oscillation tools (AOTs) are referred to as axial oscillation-supported drillstrings. Downhole vibrations induced by these tools in the drillstring are the most efficient method for friction reduction and improving axial force transfer in high-angle and extended-reach wells. Functional testing of axial oscillation tools prior to downhole operations and modeling the dynamic response of axial oscillation-supported drillstring systems are required to predict the performance and functionality of AOTs. This study presents a practical approach for functional testing of axial oscillation tools and a new analytical model for predicting the dynamic response of axial oscillation-supported drillstrings operating at surface conditions. The axial oscillation-supported drillstring is modeled as an elastic continuous system subjected to viscous damping, frictional contact, and displacement (support excitation). The functional test is a unique experimental test procedure designed to measure the pressure drop, pressure fluctuations, and axial displacement of an axial oscillation tool while varying the flow rate and the spring rate of the tool. The introduction of the spring rate as a variable in the new model and functional testing is unique to this study and not considered in the existing literature. Axial displacement and acceleration predicted from the new model closely agrees with the results obtained from the functional tests. The accuracy of the model is also validated with the results of two previously published functional tests. The comparisons demonstrate an average deviation of approximately 14.5% between predictions and measurements. The axial displacement and pressure drop of AOT increased with flow rate or oscillation frequency. The amplitude of axial displacement increased with frequency because of increased pressure drop.

The program involves the application of a novel gasification concept, termed a modular allothermal gasifier (MAG) to produce syngas from coal, biomass, and waste slurries. The MAG employs a steam-driven gasification process using a pressurized entrained flow reactor wherein the external wall surfaces are catalytically heated to 1000 °C via heterogeneous combustion of a portion of the produced syngas. The MAG can be fed by a hydrothermal treatment reactor for biomass and waste feedstocks, which employs well-developed hydrothermal processing technology using the addition of heat and water to provide a uniform slurry product. The hydrothermal treatment reactor requires no preprocessing and a clean syngas is produced at high cold gas efficiency (80%). Importantly, the MAG can operate over a wide range of positive pressures up to 3 MPa (30 bar) which provides process control to vary the output to match end-use needs or feedstock rate. The system produces minimal emissions and operates at significantly higher efficiency and lower energy requirements than pyrolysis, plasma gasification, and carbonization systems. The system is compact and modular, making it easily transportable, for example, to a variety of sites, including those where remoteness, inaccessibility, and space limitations would preclude competing systems. The system can be applied to small gasification systems without the increase in heat losses that plague conventional small scale gasifiers. Test results and model simulations are presented on a single tube system and analyses of a variety of configurations presented.

Well cements are an important aspect of wellbore integrity and recent investigations focus on describing the cement lifetime using, when possible, nondestructive tests like ultrasonic measurements. However, the original API and ASTM testing standards were based on destructive mechanical testing of cements, leading to the decision to investigate the backward and forward compatibility between ultrasonic measurements and mechanical testing, which makes the subject of this work. Ultrasonic cement measurement became a very popular method to assess the mechanical properties of the cement in a nondestructive manner. Since various measurement systems exist on the market, the development of an accurate reference data base that can be used to calibrate such measurements becomes very important. Two major systems have therefore been compared: the ultrasonic compressive strength, using the ultrasonic pulse velocity (UPV) principle, and the unconfined compressive strength (UCS), using the standard testing frame according to API and ASTM standards. The tests have been performed at different curing times, using both devices, on API Class G cements with bentonite and other additives. This paper presents the results of over 200 experiments that have displayed a different UPV response as a function of the additive content. Cement specific UPV versus UCS correlations were established. Thereby, a new level of accuracy was reached. Moreover, it was observed that after a given curing time, depending on the additive and its concentration, the UPV response is not as sensitive as the results yielded by the UCS method. The outcomes are an important step forward to improve and understand the wellbore integrity.

The study here presents laboratory testing results of Class F fly ash geopolymer for oil well cementing applications. The challenge reported in literature for the short thickening time of geopolymer ash has been overcome in this study, where more than 5 h of the thickening time is achievable. API Class H Portland cement used a controller on all the tests conducted in this work. Tests conducted in this research include unconfined compressive strength (UCS), shear bond strength, thickening time, shrinkage, free water, and cyclic and durability tests. Results indicate temperature as a crucial factor affecting the thickening time of geopolymer mix slurry. UCS testing indicates considerably higher compressive strength after one and fourteen days of curing for geopolymer mixtures. This indicates gaining strength with time for geopolymer mixture, where time retrogression effects are observed for Portland cements. Results also indicate higher shear bond strength for geopolymer mix that can better tolerate debonding issues. Additionally, more ductile material behavior and higher fracture toughness were observed for optimum geopolymer mixes. Tests also show applicability of these materials for deviated wells as a zero free water test was observed.

The current state of the art in waste heat recovery (WHR) from internal combustion engines (ICEs) is limited in part by the low temperature of the engine coolant. In the present study, the effects of operating a diesel engine at elevated coolant temperatures to improve utilization of engine coolant waste heat are investigated. An energy balance was performed on a modified three-cylinder diesel engine at six different coolant temperatures (90 °C, 100 °C, 125 °C, 150 °C, 175 °C, and 200 °C) and 15 different engine loads to determine the impact on waste heat as the coolant temperature increased. The relative brake efficiency of the engine alone decreased between 4.5% and 7.3% as the coolant temperature was increased from 90 °C to 150 °C. However, the engine coolant exergy increased between 20% and 40% over the same interval. The exhaust exergy also increased between 14% and 28% for a total waste heat exergy increase between 19% and 25%. The engine condition was evaluated after testing and problem areas were identified such as overexpansion of pistons, oil breakdown at the piston rings, and head gasket seal failure.