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Oil and gas reservoirs are of the main assets of countries possessing them. Production from these reservoirs is one of the main concerns of engineers, which can be achieved by drilling oil and gas reservoirs. Construction of hydrocarbon wells is one of the most expensive operations in the oil industry. One of the most important parameters affecting drilling cost is the rate of penetration (ROP). This paper predicts the rate of penetration using artificial intelligence and hybrid models on Kaboud oil field well #7 in the directional stage. In this study, different models were constructed through various approaches based on 1878 dataset obtained from drilling operation in the well#7. Then, the accuracy of the constructed models was compared with each other. It was found that the MLP-ABC algorithm predicts the rate of penetration more accurately, by far, as compared with other methods. The MLP-ABC algorithm achieves impressive ROP prediction accuracy (RMSE = 0.007211 m/h; AAPD = 0.1871%; R2 = 1.000 for the testing subset). Consequently, it can be concluded that this method is applicable to predict the drilling rate in that oilfield.

Accurate prediction of pore-pressures in the subsurface is paramount for successful planning and drilling of oil and gas wellbores. It saves cost and time and helps to avoid drilling problems. As it is expensive and time-consuming to measure pore-pressure directly in wellbores, it is useful to be able to predict it from various petrophysical input variables on a supervised learning basis calibrated to a benchmark wellbore. This study developed and compared three-hybrid machine-learning optimization models applied to a diverse suite of 9 petrophysical input variables to predict pore-pressure across a 273-m-thick, predominately carbonate, reservoir sequence in the giant Marun oil field (Iran) using 1972 data records. The analysis identified that the multilayer extreme learning machine model hybridized with a particle swarm optimization (MELM–PSO) applied to seven input variables by feature selection provided the most accurate pore-pressure predictions for the full dataset (RMSE = 11.551 psi (1 psi = 6.8947590868 kPa) for well MN#281). The Savitzky–Golay (SG) filter was applied to pre-process the data, and the properties were filter-ranked using the wrapping method. The MELM–PSO model outperformed the pore-pressure prediction accuracy achieved by commonly used empirical formulas involving sonic or resistivity log data or calculated pore compressibility. To further verify and generalize the applicability of the MELM–PSO model, it was applied to two other Marun oil field wells (MN#297 and MN#378) achieving RMSE prediction accuracy of 10.031 psi and 10.150 psi, respectively. These results confirmed that the trained model can be reliably applied to multiple locations across the Marun oil field for predicting pore-pressure.

The wellbore stability of a vertical well through the sandstone reservoir layers of the Asmari oil-bearing formation in south-west Iran is investigated. The safe drilling-fluid density range for maintaining wellbore stability is determined and simulated using FLAC3D software and a finite volume model established with drilled strata geomechanical features. The initiation of plastic condition is used to determine the safe mud weight window (SMWW) in specific sandstone layers. The effects of rock strength parameters, major stresses around the wellbore and pore pressure on the SMWW are investigated for this wellbore. Sensitivity analysis reveals that a reduction in cohesion and internal friction angle values leads to a significant narrowing of the SMWW. On the other hand, the reduction of pore pressure and the ratio between maximum and minimum horizontal stresses causes the SMWW to widen significantly. The ability to readily quantify changes in SMWW indicates that the developed model is suitable as a well planning and monitoring tool. Key words: wellbore stability, wellbore geomechanical property, safe mud weight window, wellbore instability risk factors, drilling stress simulation

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.

None of the various published models used to predict oil production rates through wellhead chokes from fluid composition and pressures can be considered as a universal model for all regions. Here, a model is provided to predict liquid production-flow rates for the Reshadat oil field offshore southwest Iran, applying a customized genetic optimization algorithm (GA) and standard Excel Solver non-linear and evolutionary optimization algorithms. The dataset of 182 records of wellhead choke measurements spans liquid flow rates from

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 (γg) 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.

Numerous empirical correlation models for predicting wellhead flow rates have been proposed. Here we apply a recently developed model based upon extensive data from the Ghawar Field (Saudi Arabia) to the Pazanan 1 retrograde gas-condensate field (Aghajari, Iran). A firefly optimization algorithm is applied to select the optimum coefficient values for that model by minimizing the mean square error between measured and predicted gas flow rates from a wellhead-test data set. The input data to calculate gas flow rate includes choke diameter, gas specific gravity, flowing fluid temperature, upstream and downstream pressure. The models prediction accuracy depends upon the coefficient values applied in its formula. The firefly optimization model was tested with various sensitivity cases applying different values to the key control variables γ and N (number of fireflies in the population). Optimum results in terms of minimum mean square error and rapid convergence was achieved with the control variable values γ = 2 and N = 40. The optimum case achieved with low error values and a level of accuracy that is significantly better than the predictions for dataset using the coefficient values applied to the Ghawar field, suggesting that such model coefficients need to be optimized on a field-by-field basis.

The hybrid-polymer nanocomposite poly(styrene-methyl methacrylate- acrylic acid) /nanoclay was synthesized by miniemulsion polymerization for novel use as a drilling fluid additive. Three low-solid-drilling fluids (bentonite-based; natural polymer-based; nanoclay-based) were formulated using the hybrid nanocomposite as an additive and their rheological performance compared. The polymer/clay hybrid nanoparticles significantly improve rheological and filtration properties of the drilling fluids and they remain stable at high pressure, high temperature and harsh salinity conditions. The fluids’ filtration properties improve as the concentration of the polymer/nanoclay-hybrid-nanoparticles increases. The clay-based nanocomposite additive had the highest positive impact on the rheological behavior of low-solids polymer-based drilling fluid. Analysis of rheological properties and filtration loss of the drilling fluids suggest that the optimum nanoclay concentration in the hybrid-polymer nanocomposite is about 5 wt.%.

The casing-collapse hazard is one that drilling engineers seek to mitigate with careful well design and operating procedures. However, certain rock formations and their fluid pressure and stress conditions are more prone to casing-collapse risks than others. The Gachsaran Formation in south west Iran, is one such formation, central to oil and gas resource exploration and development in the Zagros region and consisting of complex alternations of anhydrite, marl and salt. The casing-collapse incidents in this formation have resulted over decades in substantial lost production and remedial costs to mitigate the issues surrounding wells with failed casing string. High and vertically-varying horizontal stresses across the Gachsaran formation are the challenge to predict and overcome. This study investigates casing collapse in wellbores from an established petroleum geomechanics perspective to develop and compare two hybrid neural-network models, multilayer perceptron's tuned, respectively, with a genetic algorithm (MLP-GA) and a particle swarm algorithm (MLP-PSO). These machine-learning algorithms are configured to predict Poisson's ratio ( ϑ ) and maximum horizontal stress ( σ H ) from available well-log input data. A large dataset from three wellbores drilled though the Gachsaran Formation in the Marun oil field, the second largest in Iran, is used to construct and validate the hybrid MLP models. In the first step, 22323 data records from a collapsed wellbore are divided into training (15626 records; 70%) and independent testing subsets (6697 records; 30%). In the next step, in order to test whether the suggested algorithms could be generalized, the data sets of the other two wells in the field were tested. Those tests confirmed that the algorithms achieved high prediction accuracy for Poisson's ratio and maximum horizontal stress in other wellbores.

Rock shear and strain behaviors deep in the sub-surface are dependent on several geomechanical variables. These include various types of stress and rock formation moduli. Deformations of specific rock formations are usually quantified in a series of laboratory tests, which are costly and time-consuming and can only be performed on the few rock formation samples and cores available. Consequently, indirect estimates of geomechanical metrics from well-log data are required to measure variations throughout the entire length of a wellbore. Such measurements and detailed geomechanical models are required to identify zones at risk of casing collapse due to shear stress buildups. Such zones tend to occur in problematic ductile formations such as salt and swelling clays that are prone to creep. Machine-learning algorithms coupled with optimizers, and calibrated with a few mechanical rock test measurements, offer a practical method for predicting geomechanical characteristics along an entire wellbore. Hybrid models combining the optimizers, imperialist competitive (ICA) and gravitation search (GSA) algorithms, with the machine-learning algorithms, distance-weighted K-nearest neighbor (DWKNN) and multi-layer perceptron (MLP) are shown to achieve high prediction accuracy of shear modulus (Gs) from a large dataset (22,325 data records) crossing the problematic salt-rich Gachsaran formation in a wellbore from the Marun oil field (Iran). ICA-MLP, GSA-MLP, ICA-DWKNN and GSA-DWKNN models applied to four independent variables recorded by well logs were calibrated with some laboratory-derived Gs measurements. These models predict Gs with high levels of accuracy achieving coefficients of determination (R2) > 0.96. The GSA-DWKNN hybrid algorithm (R2 = 1) provided the highest prediction accuracy. Gs predictions can then be used to accurately identify zones at risk of casing collapse.