Marun Oil Field Research Articles

Scaling-up dynamic elastic logs to pseudo-static elastic moduli of rocks using a wellbore stability analysis approach in the Marun oilfield, SW Iran

Wellbore stability analysis is a critical component of petroleum engineering, evaluating the risks of sanding, reservoir compaction, and casing failures. Laboratory rock mechanical measurements must be scaled up to reservoir scales to achieve accurate results. One challenge lies in upscaling dynamic measurements from petrophysical logs to pseudo-static elastic properties, which has significant implications for oil and gas operations. We present a novel approach that combines laboratory rock mechanical measurements with well-log data to develop a mechanical earth model (MEM) for an Iranian oilfield with over 350 wells. We conducted static elastic property measurements on 40 core samples from various layers and depths of carbonate and sandstone rocks, demonstrating the practical application of our approach. By integrating these measurements with dynamic log data and static-dynamic correlations, we established a framework for evaluating the mechanical properties of different layers. Our findings indicate that the safe mud weight window ranges from 41.5 to 118.59 pcf, while the stable mud weight window ranges from 41.5 to 156 pcf. We demonstrate the importance of conducting parallel rock mechanical studies on cores and logs to reduce uncertainties, costs, and risks during oil and gas operations. We also propose a novel methodology combining lithological characteristics, abnormally high pressure, and borehole instability mechanisms to evaluate the stability of borehole walls. This framework provides a fresh perspective on wellbore stability analysis and offers practical solutions for the industry. Essential novel techniques include developing a geomechanical model that integrates laboratory rock mechanical measurements with well-log data to evaluate mechanical properties and calculate safe and stable mud-weight windows. Our study advances wellbore stability analysis by providing a new method for addressing this long-standing challenge. It offers valuable insights for petroleum engineers working in the oil and gas industry.

Genetic programming application in predicting fluid loss severity

Numerous wells worldwide encounter significant, costly, and time-consuming lost circulation issues during drilling or while deploying tubulars across naturally fractured or induced fractured formations. This can potentially lead to formation damage, wellbore instability, and even blowouts. Effectively addressing this problem and restoring fluid circulation becomes crucial to curbing non-productive time and overall operational expenses. Although numerous methods have been introduced, a universally accepted industry solution for predicting lost circulation remains absent due to the complex interplay of various factors influencing its severity. Anticipating the onset of circulation loss is imperative to mitigate its impacts, minimise costs, and reduce risks to personnel and the environment.In this study, an innovative machine learning approach employing multigene genetic algorithms is utilised to analyse a dataset of 16,970 drilling datasets from 61 wells within the Marun oil field, located in Iran, where severe loss of circulation occurred. Geological characteristics, operational drilling parameters, and the properties of the drilling fluid were all considered. The dataset encompasses 19 parameters, of which seven are chosen as inputs for predicting lost circulation incidents. These inputs are then employed to construct a predictive model, employing an 85:15 training-to-test data ratio. To assess the model's performance, unseen datasets are utilised.The novelty of this study lies in the proposed model's consideration of a concise set of relevant input parameters, particularly real-time surface drilling parameters that are easily accessible for every well. The model attains a remarkable level of prediction accuracy for fluid loss, as indicated by various performance indices. The results indicate a mean absolute error of 1.33, a root mean square error of 2.58, and a coefficient of determination of 0.968. The suggested prediction model is optimised not only for data reduction but also for universal prediction and compatibility with other existing platforms. Moreover, it aids drilling engineers in implementing suitable mitigation strategies and designing optimal values for key operational surface parameters, both prior to and during drilling operations.

A new robust predictive model for lost circulation rate using convolutional neural network: A case study from Marun Oilfield

A major cause of some of serious issues encountered in a drilling project, including wellbore instability, formation damage, and drilling string stuck – which are known to increase non-productive time (NPT) and hence the drilling cost – is what we know as mud loss. The mud loss can be prevented or at least significantly reduced by taking proper measures beforehand provided the position and intensity of such loss can be properly predicted using an accurate predictor model. Accordingly, in this study, we used the convolutional neural network (CNN) and hybridized forms of multilayer extreme learning machine (MELM) and least square support vector machine (LSSVM) with the Cuckoo optimization algorithm (COA), particle swarm optimization (PSO), and genetic algorithm (GA) for modeling the mud loss rate based on drilling data, mud properties, and geological information of 305 drilling wells penetrating the Marun Oilfield. For this purpose, we began by a pre-processing step to attenuate the effect of noise using the Savitzky-Golay method. The whole set of available data was divided into the modeling (including 2300 data points) and the validation (including 483 data points) subsets. Next, the second generation of the non-dominated sorting genetic algorithm (NSGA-II) was applied to the modeling data to identify the most significant features for estimating the mud loss. The results showed that the prediction accuracy increased with the number of selected features, but the increase became negligible when the number of selected features exceeded 9. Accordingly, the following 9 features were selected as input to the intelligent algorithms (IAs): pump pressure, mud weight, fracture pressure, pore pressure, depth, gel 10 min/gel 10 s, fan600/fan300, flowrate, and formation type. Application of the hybrid algorithms and simple forms of LSSVM and CNN to the training data (80% of the modeling data, i.e. 1840 data points) showed that all of the models tend to underestimate the mud loss at higher mud loss rates, although the CNN exhibited lower underestimation levels. Error analysis on different models showed that the CNN provided for a significantly higher degree of accuracy, as compared to other models. The more accurate outputs of the hybrid LSSVM model than those of the simple LSSVM indicated the large potentials of metaheuristic algorithms for achieving optimal solutions. The lower error levels obtained with the CNN model in the testing phase highlighted the excellent generalizability of this model for unseen data. The more accurate predictions obtained with this model, rather than the other models, in the validation phase further proved this latter finding. Therefore, application of this method to other wells in the same field is highly recommended.

Estimation of geomechanical rock characteristics from specific energy data using combination of wavelet transform with ANFIS-PSO algorithm

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.

Calcareous nannofossils of the late Eocene to early Oligocene from the Pabdeh and Asmari transition in Dezful Embayment (southwestern Iran): Evidence of a climate cooling event

Calcareous nannofossil assemblages have been investigated in the uppermost Eocene to the lowest Oligocene in the Marun oil field in the Dezful Embayment (south‐west Iran). The studied interval includes shales, marlstones and limestones. Seventeen genera and 35 species of calcareous nannofossils have been determined. Regarding the succession of nannofossil bioevents, the studied interval ranges from the late Eocene (Priabonian, CNE18 Zone or NP18 Zone) to the early Oligocene (Rupelian, CNO2 Zone, or NP22 Zone). A high relative abundance of warm water taxa (such as Sphenolithus spp., Discoaster spp. and Helicosphaera spp.) is recorded in the late Eocene, while towards the Eocene–Oligocene boundary (EOB), an increase in the relative abundance of cool and temperate taxa (such as Reticulofenestra spp., Cyclicargolithus floridanus, Dictyococcites bisecta and Markalius inversus) is identified. A marked decrease in the abundance of warm water taxa along with a decrease in species diversity indicates that the cooling event at the EOB in the Marun oil field in Iran is similar to other parts of the world (Tethys realm).

Predicting shear wave velocity from conventional well logs with deep and hybrid machine learning algorithms

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.Graphical abstract

Optimized machine learning models for natural fractures prediction using conventional well logs

Identifying and characterizing natural fractures is essential for understanding fluid flow and drainage in many oil and gas reservoirs, particularly carbonate. The presence of fractures often enhances fluid recovery but substantially complicates reservoir flow performance. Information from cores and formation imaging logs is constrained by their availability and cost. Reliably predicting fracture density using petrophysical logs and machine learning (ML) is therefore a desirable objective for fields with reservoirs displaying intermittent and sparsely distributed fractures. In the first step, Hybrid ML-optimizer models are developed and applied to a large, high- resolution, dataset (10 petrophysical variables; 3395 data records; ∼12% of the records displaying fractures) from the Asmari fractured carbonate reservoir in Iran’s Marun oil and gas field. Fracture density measured with a formation image log from one well is predicted by supervised learning using five hybrid models. The models use six of the ten petrophysical variables considered, based on feature selection, to predict fracture density. The selected variables are: corrected gamma ray (CGR), neutron porosity (NPHI); compressional sonic transition time (DT); interpreted sonic porosity (PHIS); bulk formation density (RHOB); and, the photoelectric absorption factor (PEF). The models enhance the performance of distance-weighted K-nearest neighbor (DWKNN) and neural network (MLP) with firefly and artificial bee colony optimizers. The novel firefly-KNN model achieves higher fracture density prediction accuracy than the other models. It is further refined by executing it in two layers: the first layer detects fractures; the second layer predicts fracture density. The double-layer-firefly-KNN model (DL-FF-DWKNN) achieves excellent prediction accuracy of fracture density for the Asmari carbonate (Precision = 99%, Recall = 97%, and F1-score = 98%). This correlation-free data matching technique substantially outperforms the correlation-dependent neural network models evaluated for this dataset with sparsely distributed fractured zones. The generalizability of the developed algorithms is tested with datasets from two other Marun field wells achieving prediction results that confirm high accuracy in fracture detection and prediction of fracture density (FVDC).

Implication of well logs for evaluation of a carbonate reservoir: an example, the Sarvak Formation, Zagros Basin, SW Iran

Petrophysical evaluation of well log data of the Mid Cretaceous rocks (Upper part of the Sarvak Formation) were conducted for the Marun oil field. Rock and fluid volumetric indices, such as porosity type and distribution, water saturation, and lithology, were estimated from density, resistivity, neutron porosity, sonic and gamma ray logs. Petrophysical parameters including shale volume (Vsh), total porosity (PHT), effective porosity (PHE), water saturation (Sw), hydrocarbon saturation (Sh) and lithology were calculated. Matrix Identification (MID), M-N, NPHI- RHOB, NPHI- DT cross-plots indicated that the Sarvak Formation is mainly composed of carbonate lithology. Results also indicated that shale volume varies between 0.1% and 23.3%; total and effective porosities are between 0.8% and 47.2% and between 0.1% and 45.1%, respectively. Various primary and secondary porosities do exist in the Sarvak Formation. Majority of the porosities are preferentially developed in the middle part of this oil field. Water saturation is between 2% and 100%. The results reveal that the reservoir characterization of the Sarvak Formation is very variable and fluctuating from poor to good reservoir units. Apparently, Sw increase toward the center of the reservoir and eastward as well. This is probably in part controlled by fractures that are preferentially concentrated in the middle part of the anticline.

Analysis of permeability based on petrophysical logs: comparison between heuristic numerical and analytical methods

A series of novel heuristic numerical tools were adopted to tackle the setback of permeability estimation in carbonate reservoirs compared to the classical methods. To that end, a comprehensive data set of petrophysical data including core and log in two wells was situated in Marun Oil Field. Both wells, Well#1 and Well#2, were completed in the Bangestan reservoir, having a broad diversity of carbonate facies. In the light of high Lorenz coefficients, 0.762 and 0.75 in Well#1 and Well#2, respectively, an extensive heterogeneity has been expected in reservoir properties, namely permeability. Despite Well#1, Well#2 was used as a blinded well, which had no influence on model learning and just contributed to assess the validation of the proposed model. An HFU model with the aim of discerning the sophistication of permeability and net porosity interrelation has been developed in the framework of Amaefule’s technique which has been modified by newly introduced classification and clustering conceptions. Eventually, seven distinct pore geometrical units have been distinguished through implementing the hybridized genetic algorithm and k-means algorithm. Furthermore, a K-nearest neighbors (KNN) algorithm has been carried out to divide log data into the flow units and assigns them to the pre-identified FZI values. Besides, a cross between the ε-SVR model, a supervised learning machine, and the Harmony Search algorithm has been used to estimate directly permeability. To select the optimum combination of the involved logging parameters in the ε-SVR model and reduce the dimensionality problem, a principle component analysis (PCA) has been implemented on Well#1 data set. The result of PCA illustrates parameters, such as permeability, the transit time of sonic wave, resistivity of the unflashed zone, neutron porosity, photoelectric index, spectral gamma-ray, and bulk density, which possess the highest correlation coefficient with first derived PC. In line with previous studies, the findings will be compared with empirical methods, Coates–Dumanior, and Timur methods, which both have been launched into these wells. Overall, it is obvious to conclude that the ε -SVR model is undeniably the superior method with the lowest mean square error, nearly 4.91, and the highest R-squared of approximately 0.721. On the contrary, the transform relationship of porosity and permeability has remarkably the worst results in comparison with other models in error (MSE) and accuracy (R2) of 128.73 and 0.116, respectively.

Seismic and sequence stratigraphy of the Oligocene-Miocene Asmari reservoir in the Marun oilfield, SW Iran

The Oligocene-Lower Miocene Asmari Formation shows considerable reservoir heterogeneity because of variations in the lithology, depositional facies and diagenesis. This paper aim to investigate reservoir heterogeneities using seismic, core and well logs data. Twelve carbonate microfacies and three siliciclastic petrofacies are identified in the Asmari Formation based on well log and core data from 7 wells in the Marun field, which generally indicate a shallowing-up profile. Microfacies changes suggest that the Asmari Formation was deposited in a restricted lagoonal and carbonate ramp setting with periodic clastic sediment supply due to relative sea level fall. Six third-order sequences are recognized in the Asmari Formation from studies of core and well log data, while interpretation of 3D seismic data shows that the formation consists of two second-order seismic sequences. The lowest sequence boundary between the Pabdeh and Asmari Formations (SBI) is a type-2 boundary, and the six others are type-1 sequence boundaries. Six distinct packages of reflectors are interpreted on seismic data through the Cenozoic. The Pabdeh and Asmari Formations can be divided into three packages. Package 1 corresponds to transgressive and highstand systems tract deposits, which mainly consist of shales, marls, and carbonates with interbedded sandstones. This package is overlain by mounded and lenticular seismic facies (package 2) and high-amplitude and continuous seismic reflectors (package 3). Package 2 is predominantly sandstone with interbedded shale, and package3 mainly consists of carbonates. Inversion of seismic data shows that high-porosity zones are present in the western and southern Marun field. A lower high-porosity zone corresponds to lowstand fluvial-deltaic sediments and the upper zone to the beach and shallow marine sandstones.

Predicting Formation Pore-Pressure from Well-Log Data with Hybrid Machine-Learning Optimization Algorithms

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.

Hybrid machine learning algorithms to enhance lost-circulation prediction and management in the Marun oil field

Drilling fluid loss of circulation is a challenging issue to resolve for many oil and gas wells as drilling progresses. It imposes enormous expenses on drilling industry. One of the common practices to solve this problem or make it less severe is introducing various loss circulation materials (LCM) into the circulating drilling fluid. The ability to predict the amount of mud loss before drilling a particular formation would provide engineers with invaluable information to assist them in selecting appropriate LCM properties such as particle size distribution. In this study, a number of heuristic search algorithms including genetic algorithm (GA), particle swarm size (PSO), and cuckoo search algorithm (COA) are combined with two machine-learning techniques namely multilayer perception (MLP) neural network and least square support vector machine (LSSVM) to present different hybrid algorithms in prediction of lost circulation. The established database consists of 2820 cases (datasets) extracted from available drilling reports of 305 drilled wells in the Marun oil field. These datasets are associated with drilling operation variables, formation specifications, and drilling mud characteristics which totally include 18 input variables and 1 output. Pre-processing of data involves filtering with Savitzky-Golay (SG) filter and ranking features using wrapper method. Results show that hybrid intelligent models are highly capable of predicting lost circulation before drilling a certain formation. Furthermore, LSSVM-COA and LSSVM-PSO achieve coefficients of determination of 0.9424 and 0.9391, respectively, making them the two most accurate models. GA cannot match the prediction performance of the other two algorithms in enhancing machine-learning techniques for this purpose. MLP-GA with a coefficient of determination (R2) and RMSE of 0.9304 and 25.26 (bbl/hr), respectively, yields the weakest prediction performance. Hybrid intelligent models, coupling optimizers and machine learning algorithms show themselves to be superior to applying standalone machine-learning methodologies for predicting loss of circulation.

Shear modulus prediction of embedded pressurized salt layers and pinpointing zones at risk of casing collapse in oil and gas wells

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.

A geomechanical approach to casing collapse prediction in oil and gas wells aided by machine learning

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 (σH) 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.

The effects of diagenesis on the petrophysical and geochemical attributes of the Asmari Formation, Marun oil field, southwest Iran

The distribution of good reservoir quality and its causes is the main challenges in carbonate reservoir characterization. This study investigates the effects of diagenetic processes on the reservoir quality of the carbonate successions of the Asmari Formation, in the Marun oil field, southwest Iran. The study applies an integrated approach including core petrography, petrophysical rock typing, stable carbon and oxygen isotopes as well as major and trace elements analyses. Petrographic studies and geochemical analyses express that the Asmari limestones have been affected mainly by compaction, dissolution, recrystallization, calcite and anhydrite cementation and dolomitization. Among those diagenetic overprints, dolomitization and dissolution played an important role to enhance the reservoir quality of the formation. Moreover, four types of dolomites were recognized and the rate of dolomitization increases toward the top of the Asmari carbonate successions. Possible models for dolomitization include mixing zone, brine reflux, seepage reflux and tidal pumping of seawater. Employing Flow Zone Index and Discrete Rock Type concepts led to classification of the Asmari reservoir into seven reservoir rock types. Integrating reservoir rock typing with petrographic studies and geochemical analyses also confirms that reservoir quality of the Asmari Fm. would have been mainly controlled by diagenetic processes. Moreover, stable isotopes, trace elements and facies analyses support the idea that carbonate intervals of the Asmari Formation were deposited in a warm, shallow-water environment under a saline condition.

Determining the domain of in situ stress around Marun Oil Field\u2019s failed wells, SW Iran

Accurate determination of the in situ stress domain in oil fields is of paramount importance in drilling, completion, and maintenance of wells and in petroleum geomechanics. Determination of the magnitude and direction of stresses induced by drilling around the wellbores is the first step in geomechanical studies and wellbore stability analyses. Regarding the importance of casing collapse problems in Marun Oil Field, as the first step of this investigation, geomechanical studies were conducted to determine the in situ stress domain in the failed wellbores. Using density measurements, the vertical stress (SV) was estimated to be within the range of 85–90 MPa for all wellbores. To estimate maximum-horizontal-stress (SHmax) domain, Anderson’s faulting theory and stress polygon were employed, and a value close to SV was achieved. Also, minimum horizontal stress (Shmin) was estimated using different approaches and was found to have the minimum in situ stress. Finally, the faulting regime of the areas was found to be normal/strike slip, where the stress values are close to each other due to salt lithology and high pore pressures in the Gachsaran Formation and thereby could be assumed as hydrostatic stresses.

Electrofacies analysis of the Asmari reservoir, Marun oil field, SW Iran

This article integrates core and well log data to determine reservoir electrofacies of the Oligo-Miocene Asmari Formation in the western Dezfol Embayment, SW Iran. At the start, an unsupervised neural network was employed based on the selforganizing map (SOM) technique to identify and extract electrofacies groups of Asmari Formation in the Marun oilfield, which is a mixed siliciclastic and carbonate reservoir. Using inexpensive and accessible petrophysical wireline logs, such as gamma ray, sonic, density and neutron, along with calculated reservoir data (water saturation and effective porosity) from 12 reference wells and their correlation with reservoir core data, led to recognition of seven electrofacies including first four class as limestone rocks (EF1–EF4) and others three classes as sandstone rocks (EF5–EF7). Based on the porosity and permeability maps and water saturation at different depth levels, EF4 and EF7 with low porosity and permeability, high water saturation are considered as non-reservoir relating mostly to sedimentary textures of mudstone and argillaceous sandstones, respectively. By contrast, EF1 and EF5 with high values of porosity, permeability and low percent of water saturations characterize the best reservoir quality rocks, and EF2, EF3 and EF6 have medium reservoir quality. EF1 consists of dolomitic skeletal grainstone, in which biomoldic, vuggy and intercrystalline porosity is the dominant pore type, while the intergranular porosity is the major pore type in EF4 as an unconsolidated sand electrofacies. In general, the results obtained in this study indicate a satisfactory agreement between core data and log facies. This correlation allows rockfacies to be classified in the cored wells and predict those facies in the uncored wells. Using the methodology outlined here, it is possible to track reservoir electrofacies from conventional well log data.

Landfill site selection for solid drilling wastes (case study: Marun oil field, southwest Iran)

It is important to manage the oil wastes due to their chemical composition and the risks to human life. When the inappropriate management of such wastes causes environmental pollution and threatens all life, the importance of this issue becomes apparent. Landfill sites, in accordance with environmental laws, are among the management methods of oil wastes. The current study aimed to evaluate landfill site selection for solid drilling waste of Marun oilfield. An area with a 30 km radius was identified in the center of Marun oilfield for site selection based on environmental conditions and economic parameters. Effective parameters in the site selection process were determined and collected; then, the database of the collected data was prepared using GIS software. To implement the ordered weighted average method for site selection, the effective parameters as information layers were standardized using fuzzy logic and the relative weight of each of them was determined using the analytical hierarchy process. The limitations of each parameters were standardized using Boolean logic. Based on the sequential weights that calculated using the ordered weighted average method, seven scenarios were defined for determining the appropriate sites. Each scenario was classified into five classes of absolutely unsuitable, unsuitable, medium suitable, suitable, and absolutely suitable. Based on the characteristics of the oil wastes, the sites with absolutely suitable class in scenarios 6 and 7 were selected as proposed sites that include 6 sites and covers 3% of the total area of the studied range. The selected sites were prioritized using the ELECTRE method. The results showed that sites that are more suitable in terms of economic parameters, lithology features, and land use are given the highest priority.

An Evaluation of Rock Integrity and Fault Reactivation in the Cap Rock and Reservoir Rock Due to Pressure Variations

Cap rocks are dams which can prevent the upward movement of hydrocarbons. They have disparities and weaknesses including discontinuities, crushed areas, and faults. Gas injection is an effective mechanism for oil recovery and pore pressure. With increasing pore pressure, normal stress is reduced, and the integrity of impermeable boundaries (cap rock, fault, etc.) becomes instable. A successful strategy for reservoir development is the inevitable necessity of conducting geomechanical studies and modeling the reservoir. The construction of a comprehensive geomechanical model, including the stress state is a function of depth (direction and amount), physical properties of the reservoir rock and its formations (rock resistance and elastic moduli), pore pressure estimation, and description and distribution of fractures and faults. In this work, analytical and numerical methods have been used in geomechanical modeling, and the data used for modeling and petrophysical information are downhole tests. The geomechanical modeling of gas injection into the reservoir and, simultaneously, the operation of Asmari reservoir and Marun oilfield cap rock in the southwest of Iran were carried out. The threshold of reactivating faults and the critical pressure of induced fracture were calculated, and the results were presented as analytical and numerical models. Moreover, in addition to analyzing the stress field at depths, the resistance parameters of the formations were determined. The results showed that the most changes and instabilities were around the wellheads, fractures, and the edges of the field.

Application of decision tree, artificial neural networks, and adaptive neuro-fuzzy inference system on predicting lost circulation: A case study from Marun oil field

Abstract One of the most prevalent problems in drilling industry is lost circulation which causes intense increase in drilling expenditure as well as operational obstacles such as well instability and blowout. The aim of this research is to develop smart systems for estimating amount of lost circulation making able to use appropriate prevention and remediation methods. To obtain this aim, a large data set were collected from 61 recently drilled wells in Marun oil field in Iran to be used for developing relevant models. After that, using the extracted data set consisting of 1900 data subset, intelligent prediction models including decision tree (DT), adaptive neuro-fuzzy inference systems (ANFIS), artificial neural networks (ANN) and also hybrid artificial neural network namely genetic algorithm-multi-layer perception (GA-MLP) were developed to make a quantitative prediction on lost circulation. The model outputs are then analyzed by various performance indices such as variance accounted for (VAF), root mean square error (RMSE), performance index (PI) and coefficient of determination (R2). Eventually, it is found that developed models are highly applicable in lost circulation prediction. Concluding remark is that DT model having determination coefficient of 0.9355 and RMSE of 0.091 is superior comparing to other developed models and hybrid ANN (GA-MLP) exhibits lowest prediction performance among other implemented models.