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Mud logging data, which quantify the energy expended in rock breaking during drilling, are routinely acquired during drilling. These data offer a practical means to estimate the geomechanical properties of rocks, particularly confined compressive strength (CCS), avoiding the need for costly and destructive laboratory tests. Thus, in this study, predictive models for CCS were developed utilizing mudlogging data and employing various machine learning (ML) algorithms, including multi-layer perceptron neural network (MLPNN), least squares support vector machine (LSSVM), Gaussian process regression (GPR), and random forest (RF). Data were compiled from reservoir sections of two vertical wells (Well A and Well B) of an oil field in southwest Iran. CCS values were estimated using petrophysical logs and empirical correlations, calibrated with laboratory test results, and organized into a database of input and output features. 80% of Well A data were allocated for training, with 20% reserved for testing. The Tukey method was then used to remove outliers from the training subset, ensuring a high-accuracy and generalizable model. Subsequently, tuned ML algorithms based on the problem condition were applied to develop predictive CCS models using the training data. These models were then rigorously assessed using the test data. The outcomes of these steps revealed that the GPR-based model exhibited the lowest Root Mean Square Error (RMSE, train: 0.8499 MPa and test: 6.3886 MPa) and the highest coefficient of determination (R2, train: 0.9993 and test: 0.9597) among developed ML models. Further scrutiny employing over-fitting index and regression error characteristic curve techniques underscored the high accuracy and the generalizability of the GPR model on unseen data. Application of the GPR model on Well B demonstrated remarkable accuracy (RMSE = 2.3107 MPa) and its real-time predictive capability from minimal mudlogging data variables, providing a reliable and cost-effective solution for CCS prediction in vertical wells. Highlights: 4 machine learning models predict confined compressive strength (CCS) from mud-log data 17 outliers detected with high mechanical specific energy values in CCS prediction Gaussian process regression (GPR) delivers the most precise CCS predictions Depth and weight on bit are the most and least influential input features on CCS Partial-dependence plots reveal key input influences on CCS predictions" [ABSTRACT FROM AUTHOR]

The geomechanical characteristics of a drill formation are uncontrollable factors that are crucial to determining the optimal controllable parameters for a drilling operation. In the present study, data collected in wells drilled in the Marun oilfield of southwestern Iran were used to develop adaptive network-based fuzzy inference system (ANFIS) models of geomechanical parameters. The drilling specific energy (DSE) of the formation was calculated using drilling parameters such as weight-on-bit (WOB), rate of penetration (ROP), rotational speed of drilling string (RPM), torque, bit section area, bit hydraulic factor, and bit hydraulic power. A stationary wavelet transform was subsequently used to decompose the DSE signal to the fourth level. The approximation values and details of each level served as inputs for ANFIS models using particle swarm optimization (PSO) algorithm and genetic algorithm (GA). As model outputs, the Young's Modulus, uniaxial compressive strength (UCS), cohesion coefficient, Poisson's ratio, and internal friction angle were compared to the geomechanical parameters obtained from petrophysical logs using laboratory-developed empirical relationships. Both models predicted the Young's modulus, UCS, and cohesion coefficient with high accuracy, but lacked accuracy in predicting the internal friction angle and Poisson's ratio. The root mean square error (RMSE) and determination coefficient (R2) were lower for the ANFIS-PSO model than for the ANFIS-GA model, indicating that the ANFIS-PSO model presents higher accuracy and better generalization capability than the ANFIS-GA model. As drilling parameters are readily available, the proposed method can provide valuable information for strategizing a drilling operation in the absence of petrophysical logs. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO 2) capture is promising with economic and environmental benefits as an active climate-change mitigation approach. Nevertheless, the low adsorption capacity of traditional materials such as zeolites remains challenging. Metal-organic framework (MOF) has been proposed as a remedy, but its laboratory screening is costly and time-consuming. Hybrid machine learning and optimization models offer precise and rapid predictions of CO 2 uptake, effectively addressing the issue of inadequate hyperparameter tuning often seen in standalone machine learning models. In this study, hybrid machine-learning models are employed to accurately predict the CO 2 adsorption capacity of MOFs required for swift screening of MOF candidates, expediting the discovery of novel materials and fostering a more comprehensive understanding of CO 2 adsorption mechanisms. Six models, including multi-layer perceptron neural network (MLPNN) and least square support vector machine (LSSVM) algorithms, along with their hybrid counterparts utilizing particle swarm optimization (PSO) and growth optimization (GO) algorithms, are evaluated based on experimental data from previous studies. The experimental data consists of 475 data points and six input variables: Brunauer-Emmett-Teller specific surface area (SBET), pressure (P), temperature (T), Langmuir-specific surface area (SL), metal center (MC), and total pore volume (VT). Results demonstrate that the LSSVM-GO model outperforms the others, with an overall root mean squared error of 0.7484 and an R2 of 0.9798. The LSSVM-GO model's applicability domain is verified using a Williams plot, and a feature importance analysis conducted revealed that SBET has the greatest effect and VT has the least effect on its MOF CO 2 -uptake predictions. The novelty of this study lies in developing a machine-learning-based approach that provides an accurate and cost-effective prediction of adsorption capacity for different MOF design experiments. This approach has the potential to significantly reduce screening costs and time required for various MOF designs, making it a more viable carbon capture strategy. • The CO 2 uptake of MOFs predicted from six influential variables. • Predictions generated from four hybrid machine learning models. • MOF CO 2 uptake predicted with high precision by the LSSVM-GO model. • SHAP analysis identifies specific surface area as the most influential input variable. • Partial dependencies between input variables reveal non-linear relationships. [ABSTRACT FROM AUTHOR]

Shear wave velocity (VS) data from sedimentary rock sequences is a prerequisite for implementing most mathematical models of petroleum engineering geomechanics. Extracting such data by analyzing finite reservoir rock cores is very costly and limited. The high cost of sonic dipole advanced wellbore logging service and its implementation in a few wells of a field has placed many limitations on geomechanical modeling. On the other hand, shear wave velocity VS tends to be nonlinearly related to many of its influencing variables, making empirical correlations unreliable for its prediction. Hybrid machine learning (HML) algorithms are well suited to improving predictions of such variables. Recent advances in deep learning (DL) algorithms suggest that they too should be useful for predicting VS for large gas and oil field datasets but this has yet to be verified. In this study, 6622 data records from two wells in the giant Iranian Marun oil field (MN#163 and MN#225) are used to train HML and DL algorithms. 2072 independent data records from another well (MN#179) are used to verify the VS prediction performance based on eight well-log-derived influencing variables. Input variables are standard full-set recorded parameters in conventional oil and gas well logging data available in most older wells. DL predicts VS for the supervised validation subset with a root mean squared error (RMSE) of 0.055 km/s and coefficient of determination (R2) of 0.9729. It achieves similar prediction accuracy when applied to an unseen dataset. By comparing the VS prediction performance results, it is apparent that the DL convolutional neural network model slightly outperforms the HML algorithms tested. Both DL and HLM models substantially outperform five commonly used empirical relationships for calculating VS from Vp relationships when applied to the Marun Field dataset. Concerns regarding the model's integrity and reproducibility were also addressed by evaluating it on data from another well in the field. The findings of this study can lead to the development of knowledge of production patterns and sustainability of oil reservoirs and the prevention of enormous damage related to geomechanics through a better understanding of wellbore instability and casing collapse problems. [ABSTRACT FROM AUTHOR]

Effective management of solid content in drilling fluids is crucial for minimizing operational challenges and ensuring high efficiency during drilling operations. Accurate and timely quantification of solid content remains a challenge due to the lengthy laboratory measurement techniques currently in use. As a result, existing solid content measurement methods lack real-time capabilities, hindering proactive monitoring of this parameter. This study develops integrated machine learning and optimization models to predict solid content based solely on two drilling fluid properties that can be quickly measured at the well site—fluid density and Marsh funnel viscosity. A dataset of 1290 records was compiled from drilling fluid compositions used in drilling 17 wells across various lithological formations in two fields in southwest Iran. The Least Squares Support Vector Machine and Radial Basis Function Neural Network models are integrated with Particle Swarm Optimization and Grey Wolf Optimizer to create efficient and rapid solid content prediction models. Six intelligent model configurations, including four hybrid machine learning-optimizer models, were trained and evaluated for their prediction and generalization performance. The hybrid models outperformed the standalone models, with the Least Squares Support Vector Machine-Grey Wolf Optimizer model achieving the lowest prediction error (root mean square error = 1.220 % with the testing dataset). Feature importance analysis reveals that fluid density has a more significant impact on solid content predictions than Marsh funnel viscosity. These novel two-variable hybrid models can be applied in well-site drilling fluid laboratories to enable frequent control and effective management of solid content during drilling operations. [Display omitted] • Prompt drilling fluids solid content prediction and monitoring. • Integrated machine learning optimization models suitable for well-site implementation. • Predictive models receive two inputs: fluid density and Marsh funnel viscosity. • Optimized least-squares support vector machine reliably predicts solid content. [ABSTRACT FROM AUTHOR]

Awareness of uniaxial compressive strength (UCS) as a key rock formation parameter for the design and development of gas and oil field plays. It plays an essential role in the selection of the drill bits and stability of the wellbore's wall. Precise prediction of UCS before or during the drilling, especially in exploration wellbores, is necessary to improve the drilling speed and reduce the instability of the wellbore walls. UCS predictor machine-learning (ML) models are developed in this study using drilling parameters recorded during drilling using least-squares support-vector machine (LSSVM) and multi-layer extreme learning machine (MELM) algorithms hybridized with cuckoo optimization algorithm (COA), particle swarm optimization (PSO) and genetic algorithm (GA) optimizers. In addition, stand-alone LSSVM and convolutional neural network (CNN) models without optimizer enhancements are evaluated. Drilling and petrophysical data recorded for two wells (A and B) from the Rag-e-Safid oil field in southwest Iran were compiled to form the studied dataset. UCS was initially calculated numerically based on data from laboratory tests from petrophysical logs. The Well A dataset was pre-processed to remove outlying data records by applying the quantile regression algorithm. That analysis indicated that 9 data records should be removed from the Well A dataset. A decision tree model was employed for feature selection purposes to identify the more influential variables with respect to UCS. Depth, weight on the drill bit (WOB), drill-string rotation speed (RPM), rate of penetration (ROP), and torque (Trq) were the variables identified as being highly influential on UCS values. Application of the ML models on the training data subset (75% of Well A data records) revealed that the MELM-COA algorithm achieved the lowest root mean squared error (4.6945 MPa) and a higher coefficient of determination (0.9873) value than the other models when predicting UCS in the Well A training and validation data subsets. The Well-A-trained MELM-COA model confirmed its generalizability within the studied field by generating low UCS prediction errors when applied to the independent Well B testing dataset. [ABSTRACT FROM AUTHOR]

Emissions of carbon dioxide (CO 2) are a major source of atmospheric pollution contributing to global warming. Carbon geological sequestration (CGS) in saline aquifers offers a feasible solution to reduce the atmospheric buildup of CO 2. The direct determination of the trapping efficiency of CO 2 in potential storage formations requires extensive, time-consuming simulations. Machine-learning (ML) models offer a complementary means of determining trapping indexes, thereby reducing the number of simulations required. However, ML models have to date found it difficult to accurately predict two specific reservoir CO2 indexes: residual-trapping index (RTI) and solubility-trapping index (STI). Hybridizing ML models with optimizers (HML) demonstrate better RTI and STI prediction performance by selecting the ML model's hyperparameters more precisely. This study develops and evaluates six HML models, combining a least-squares-support-vector machine (LSSVM) and a radial-basis-function neural network (RBFNN) with three effective optimizer algorithms: genetic (GA), cuckoo optimization (COA), and particle-swarm optimization (PSO). 6810 geological-formation simulation records for RTI and STI were compiled from published studies and evaluated with the six HML models. Error and score analysis reveal that the HML models outperform standalone ML models in predicting RTI and STI for this dataset, with the LSSVM-COA model achieving the lowest root mean squared errors of 0.00421 and 0.00067 for RTI and STI, respectively. Sensitivity analysis identifies residual gas saturation and permeability as the most influential input variables on STI and RTI predictions. The high RTI and STI prediction accuracy achieved by the HML models offers to reduce the uncertainties associated with CGS projects substantially. • 6810-record dataset compiled for CO 2 -trap analysis in deep geological formations. • Optimized machine learning (ML) models predict two CO 2 trapping indexes. • Evolutionary optimizers assist in tuning ML model hyperparameters. • COA-optimized least-squares support-vector machine provides best predictions. • Hybridized ML models substantially outperform standalone ML models. [ABSTRACT FROM AUTHOR]

[Display omitted] • 2072-record dataset compiled to predict H 2 uptake by porous carbon material (PCM) • PCM H 2 uptake predictions of four machine learning (ML) models compared. • Least-squares support-vector machine generates the most precise PCM H 2 uptake. • Pressure and temperature are the most influential variables for PCM H 2 uptake. • ML provides a rapid technique to complement time-consuming H 2 uptake experiments. Hydrogen (H 2) absorption percentage by porous carbon media (PCM) is important for identifying efficient H 2 storage media. PCM with H 2 -uptakes of greater than 5 wt% are urgently required to improve the performance of H 2 fuel tanks for use in fuel-cell-powered transportation vehicles. Machine-learning (ML) methods can provide effective tools for predicting PCM H 2 -uptakes from influential variables determined by experiments performed on a wide range of PCM. This study evaluates the PCM-H 2 -uptake prediction performance of four well-established ML models: generalized-regression neural network (GRNN), Least-squares-support-vector machine (LSSVM), adaptive-neuro-fuzzy-inference system (ANFIS), and extreme-learning machine (ELM). A 2072-record database, compiled from literature, comprising eleven independent variables and PCM H 2 -uptake (dependent variable covering a range of 0 to 8.38 wt%) was evaluated by the four ML models. Each model was trained and validated using 10-fold cross-validation. The LSSVM generates the best PCM-H 2 -uptake prediction performance when applied to an independent testing subset of data records, achieving a root mean squared error of just 0.2407 wt%. Feature importance sensitivity analysis identifies pressure as the most influential of the independent variable considered. Leverage analysis identified that 96.53% of the data records of the compiled database, when predicted by the LSSVM model, resided within the applicable domain with only seventy-two data records considered as suspected outliers. These results indicate that the LSSVM model developed is highly generalizable for the purpose of predicting PCM H 2 -uptake from the influential variables. [ABSTRACT FROM AUTHOR]

Carbonate petroleum reservoirs typically have lower permeabilities and recovery factors than sandstone reservoirs, so the natural fractures they often incorporate have positive impacts on resource recovery and fluid production rates. Quantifying effective permeability, incorporating contributions from pores and fractures, is therefore essential in the reservoir characterization and flow-regime modelling of carbonate reservoirs. This research applies a robust machine-learning forecasting model to predict permeability (K) for heterogeneous carbonate gas condensate reservoirs. A 212-point dataset from six gas-condensate carbonate reservoirs (Russia and Iran) is compiled. The input variables considered are porosity (Φ, %), specific surface area (Sp , 1/cm) and irreducible water saturation (S wir , %). These variables are assessed using four machine learning models: group method of data handling (GMDH), polynomial regression (PR), support vector machine (SVR), and decision tree (DT) to predict permeability. The GMDH algorithm, a polynomial neural network with a customized architecture is developed, such that it displays increased prediction accuracy and improved learning capabilities. All four models developed in this study substantially improve upon K predictions derived from established empirical correlations. The GMDH model also outperforms the other models in respect of K prediction accuracy using Φ, Swir , and Sp as input variables. It achieves permeability prediction accuracy for the multi-field dataset evaluated with a root mean squared error (RMSE) and coefficient of determination (R2) for the training and testing of the best model (GMDH) of RMSE = 9.2 mD and R2 = 0.9988; RMSE = 0.4 mD and R2 = 0.9972, respectively. The model can be readily adapted for application to other field datasets to estimate K from limited well-log and/or core data. • Group method of data handling provides accurate predictions of permeability. • Carbonate reservoir permeability predicted with porosity & specific surface area. • Gas-condensate reservoirs predicted without need for water saturation data. • 212 core datasets from large gas condensate fields in Iran and Russia compiled. • Empirical models poorly predict permeability with just porosity/water saturation. [ABSTRACT FROM AUTHOR]

Gas condensate reservoirs display unique phase behavior and are highly sensitive to reservoir pressure changes. This makes it difficult to determine their PVT characteristics, including their condensate viscosity, which is a key variable in determining their flow behavior. In this study, a novel machine learning approach is developed to predict condensate viscosity in the near wellbore regions (μ c) from five input variables: pressure (P), temperature (T), initial gas to condensate ratio (RS), gas specific gravity (γg), and condensate gravity (API). Due to the absence of accurate recombination methods for determining μc machine learning methods offer a useful alternative approach. Nine machine learning and hybrid machine learning algorithms are evaluated including novel multiple extreme learning machine (MELM), least squares support vector machine (LSSVM) and multi-layer perceptron (MLP) and each hybridized with a particle swarm optimizer (PSO) and genetic algorithm (GA). The new MELM algorithm has some advantages including 1) rapid execution, 2) high accuracy, 3) simple training, 4) avoidance of overfitting, 5) non-linear conversion during training, 6) no trapping at local optima, 6) fewer optimization steps than SVM and LSSVM. Combining MELM with PSO, to find the best control parameters, further improves its performance. Analysis indicates that the MELM-PSO model provides the highest μ c prediction accuracy achieving a root mean squared error (RMSE) of 0.0035 cP and a coefficient of determination (R2) of 0.9931 for a dataset of 2269 data records compiled from gas-condensate fields around the world. The MELM-PSO algorithm generates no outlying data predictions. Spearman correlation coefficient analysis identifies that P, γg and Rs are the most influential variables in terms of condensate viscosity based on the large dataset studied. • 2269 viscosity data records compiled from worldwide gas condensate fields. • Machine learning with optimizers reliably predicts condensate viscosity (μc). • Empirical models do not accurately predict μc and are not fit for purpose. • MELM-PSO model yields best μc predictions outperforming other models. • Pressure, gas gravity and condensate/gas ratio have greatest influence. [ABSTRACT FROM AUTHOR]

Measuring fluid flow rate passing through pipelines is a basic strategy for developing the infrastructure of fluid-dependent industries. It is a challenging issue for trade, transportation, and reservoir management purposes. Predicting the flow rate of fluid is also regarded as one of the crucial steps for the development of oil fields. In this study, a novel deep machine learning model, convolutional neural network (CNN), was developed to predict oil flow rate through orifice plate (Qo) from seven input variables, including fluid temperature (T f), upstream pressure (Pu), root differential pressure (√ΔP), percentage of base sediment and water (BS&W%), oil specific gravity (SG), kinematic viscosity (ν), and beta ratio (β, the ratio of pipe diameter to orifice diameter). Due to the absence of accurate and credible methods for determining Qo, deep learning can be a useful alternative to traditional machine learning methods. Justifying the promising performance of the developed CNN model over conventional machine learning models, three different machine learning algorithms, including radial basis function (RBF), least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM), were also developed and their prediction performance was compared with that of the CNN model. A sensitivity analysis was also performed on the influence degree of each input variable on the output variable (Qo). The study outcomes indicate that the CNN model provided the highest Qo prediction accuracy among all the four models developed by presenting a root mean squared error (RMSE) of 0.0341 m3/s and a coefficient of determination (R2) of 0.9999, when applied to the dataset of 3303 data records compiled from oil fields around Iran. The Spearman correlation coefficient analysis results display that √ΔP, Pu, and Tf were the most influential variables on the oil flow rate in respect of the large dataset evaluated. • A dataset containing 3303 data records collected from 12 oil production units in southwest Iran is evaluated. • Machine learning techniques accurately predict oil flow rate (Qo) through orifice plates. • CNN technique makes the most accurate Qo predictions. • Root differential pressure (√ΔP),upstream pressure (P u), and fluid temperature (T f) are the most influential input variables. [ABSTRACT FROM AUTHOR]

An important requirement of reservoir management is to understand the properties of reservoir fluids and dependent phase behaviors. This makes it possible to determine the properties of reservoir fluids in laboratory pressure-volume-temperature (PVT) tests. Such laboratory tests are costly and time consuming, and reservoir data are sometimes not available. When estimating the in-place fluid volumes and/or designing enhanced recovery processes information on bubble point pressure (BPP) and oil formation volume factor (OFVF) is required. Predicting these two parameters is therefore one of the priorities of reservoir engineers. It is becoming increasingly beneficial to be able to predict BPP and OFVF with efficient machine-learning algorithms based on underlying variables that are more easily measured directly in the field. In this study, a dataset of 638 data records of published crude oil fluid samples from around the world is evaluated. After filtering the dataset for outliers, 591 data records for BPP and 599 datasets for OFVF are evaluated with efficient hybrid machine-learning algorithms (multi-layer extreme learning machine --MELM-- and least squares support vector machine -- LSSVM) optimized using a genetic algorithm –GA-- and a particle swarm optimizer –PSO-- to improve their prediction performance. Four underlying variables are considered for each data record: temperature (T), solution gas oil ratio (Rs), gas specific gravity (γ g) and oil gravity (API). The PSO- MELM-PSO hybrid algorithm achieved the highest prediction accuracy measured in terms of root mean square errors (RMSE) of 33.5 psi for BPP and 0.0199 for OFVF. The four-hybrid machine-learning-optimization algorithms evaluated all outperform the empirical relationships used for many decades in the oil industry to predict BPP and OFVF. • Hybrid machine-learning with optimization achieve high prediction accuracy. • Bubble point pressure & formation volume factor predicted for 567 crude oils. • Multi-layer extreme learning machine with particle swarm optimizer works best. • MELM-PSO outperforms traditional empirical correlations in prediction accuracy. • Solution gas oil ratio is the most influential of four input variables. [ABSTRACT FROM AUTHOR]

• Prediction of oil flow through orifice plate meters based on multiple input variables. • Comparison of machine-learning-optimizers to predict oil flow through orifice plates. • Two-stage DWKNN-ABC Plus MLP-FF model yields most accurate flow predictions. • Machine-learning-optimizer algorithms avoid problematic discharge coefficients. • Nearest-neighbor model assigns high weights to some poorly correlated variables. Flow measurement is an essential requirement for monitoring and controlling oil movements through pipelines and facilities. However, delivering reliably accurate measurements through certain meters requires cumbersome calculations that can be simplified by using supervised machine learning techniques exploiting optimizers. In this study, a dataset of 6292 data records with seven input variables relating to oil flow through 40 pipelines plus processing facilities in southwestern Iran is evaluated with hybrid machine-learning-optimizer models to predict a wide range of oil flow rates (Qo) through orifice plate meters. Distance-weighted K-nearest-neighbor (DWKNN) and multi-layer perceptron (MLP) algorithms are coupled with artificial-bee colony (ABC) and firefly (FF) swarm-type optimizers. The two-stage ABC-DWKNN Plus MLP-FF model achieved the highest prediction accuracy (root mean square errors = 8.70 stock-tank barrels of oil per day) for oil flow rate through the orifice plates, thereby removing dependence on unreliable empirical formulas in such flow calculations. [ABSTRACT FROM AUTHOR]

A Takagi-Sugeno adaptive neuro-fuzzy inference system (TSFIS) model is developed and applied to a dataset of wellhead flow-test data for the Resalat oil field located offshore southern Iran, the objective is to assist in the prediction and control of multi-phase flow rates of oil and gas through the wellhead chokes. For this purpose, 182 test data points (Appendix 1) related to the Resalat field are evaluated. In order to predict production flow rate (Q L) expressed as stock-tank barrels per day (STB/D), this dataset includes four selected input variables: upstream pressure (Pwh); wellhead choke sizes (D64); gas to liquid ratio (GLR); and, base solids and water including some water-soluble oil emulsion (BS&W). The test data points evaluated include a wide range of oil flow rate conditions and values for the four input variables recorded. The TSFIS algorithm applied involves five data processing steps: a) pre-processing, b) fuzzification, c) rules base and adaptive neuro-fuzzy inference engine, d) defuzzification, and e) post-processing of the fuzzy model. The developed TSFIS model for the Resalat oil field database predicted oil flow rate to a high degree of accuracy (root mean square error = 247 STB/D, correlation coefficient = 0.9987), which improves substantially on the commonly used empirical algorithms used for such predictions. TSFIS can potentially be applied in wellhead choke fuzzy controllers to stabilize flow in specific wells based on real-time input data records. • Takagi-Sugeno fuzzy inference system predicts choke fluid flow rates. • Adaptive neuro-fuzzy algorithm assists in optimizing fuzzy logic rules. • Flow rates through wellhead choke predicted with high accuracy with 4 input variables. • Upstream pressure, choke size, gas/liquid, base solids & water are the key inputs. • Fuzzy inferences system is readily adapted for use with fuzzy choke controllers. [ABSTRACT FROM AUTHOR]