Use of Attributes on 3D Seismic Data for Studying Mishrif Formation in East Abu-Amoud Field, South-Eastern Iraq

The seismic method depends on the nature of the reflected waves from the interfaces between layers, which in turn depends on the density and velocity of the layer, and this is called acoustic impedance. The seismic sections of the East Abu-Amoud field that is located in Missan Province, south-eastern Iraq, were studied and interpreted for updating the structural picture of the major Mishrif Formation for the reservoir in the field. The Mishrif Formation is rich in petroleum in this area, with an area covering about 820 km2. The horizon was calibrated and defined on the seismic section with well logs data (well tops, check shot, sonic logs, and density logs) in the interpretation process to identify the upper and lower boundaries of the Formation. Seismic attributes were used to study the formation, including instantaneous phase attributes and relative acoustic impedance on time slice of 3D seismic data . Also, relative acoustic impedance was utilized to study the top of the Mishrif Formation. Based on these seismic attributes, karst features of the formation were identified. In addition, the nature of the lithology in the study area and the change in porosity were determined through the relative acoustic impedance The overlap of the top of the Mishrif Formation with the bottom of the Khasib Formation was determined because the Mishrif Formation is considered as an unconformity surface.

Predict Geomechanical Parameters with Machine Learning Combining Drilling Data and Gamma Ray

The aim of this work is the development of a fast and reliable method for geomechanical parameters evaluation while drilling using surface logging data. Geomechanical parameters are usually evaluated from cores or sonic logs, which are typically expensive and sometimes difficult to obtain. A novel approach is here proposed, where machine learning algorithms are used to calculate the Young's Modulus from drilling parameters and the gamma ray log. The proposed method combines typical mud logging drilling data (ROP, RPM, Torque, Flow measurements, WOB and SPP), XRF data and well log data (Sonic logs, Bulk Density, Gamma Ray) with several machine learning techniques. The models were trained and tested on data coming from three wells drilled in the same basin in Kuwait, in the same geological units but in different reservoirs. Sonic logs and bulk density are used to evaluate the geomechanical parameters (e.g. Young's Modulus) and to train the model. The training phase and the hyperparameter tuning were performed using data coming from a single well. The model was then tested against previously unseen data coming from the other two wells. The trained model is able to predict the Young's modulus in the test wells with a root mean squared error around 12 GPa. The example here provided demonstrates that a model trained with drilling parameters and gamma ray coming from one well is able to predict the Young Modulus of different wells in the same basin. These outcomes highlight the potentiality of this procedure and point out several implications for the reservoir characterization. Indeed, once the model has been trained, it is possible to predict the Young's Modulus in different wells of the same basin using only surface logging data.

Lithological Study of Mishrif, Rumaila, Ahmadi and Mauddud Formations Using Vp/ Vs Ratio, Acoustic Impedance and Well Logging at Amara Oil Field, Southern Iraq

The lithology of four formations from the Cretaceous period (Mishrif, Rumaila, Ahmadi, and Mauddud) was evaluated using the Acoustic Impedance and Vp/Vs ratio cross plot from Rock Physics Templates. Dipole sonic logs in Am-6-Am-10 well log were used to calculate compression velocity then the estimated shear velocity using Greenberg Castagna equations. RHOB and VP logs were used to calculate Acoustic Impedance. The ratio of Vp/Vs was measured then used with Acoustic Impedance colored by shale volume which is measured from gamma ray log, porosity and water saturation to estimate lithology type of the considered formations using cross plots and rock physics chart in the Techlog software. The lithology of the formations found to be of high porosity limestone alternating with hard limestone layers and the shale volume increases in the Ahmadi formation. The water bearing zone was found in all Formations, this zone is indicted by high Vp/ Vs ratio and low AI. The hydrocarbon bearing zones were indicated by low amount of both Acoustic Impedance and Vp/Vs ratio and this observation was shown in Mishrif and Mauddud formations.

Structural Interpretation of Seismic Data of Mishrif Formation in East Abu-Amoud Field, South-eastern Iraq

The seismic method depends on the nature of the reflected waves from the interfaces between layers, which in turn depends on the density and velocity of the layer, and this is called acoustic impedance. The seismic sections of the East Abu-Amoud field that is located in Missan Province, south-eastern Iraq, were studied and interpreted for updating the structural picture of the major Mishrif Formation for the reservoir in the Abu-amoud field. The Mishrif Formation is rich in petroleum in this area, with an area covering about 820 km2. The seismic interpretation of this study was carried out utilizing the software of Petrel-2017. The horizon was calibrated and defined on the seismic section with well-logs data (well tops, check shot, sonic logs, and density logs) in the interpretations process for identifying the upper and lower boundaries of Mishrif Formation. As well, mapping of two-way time and depth structural maps was carried out, to aid in understanding the lateral and vertical variations and to show the formation of the structural surfaces. The study found that Mishrif thickness increases toward the east, which means that it increases from the Abu-Amoud field in Nasiriyah towards the East Abu-Amoud field in Missan province. The aim of the study is to draw a high-resolution structural image of the East Abu Amoud field in southeast Iraq and to show the types of the existing faults and structures in the study area.

Real-Time Prediction for Sonic Slowness Logs from Surface Drilling Data Using Machine Learning Techniques

Acoustic data obtained from sonic logging tools plays an important role in formation evaluation. Given the associated costs, however, the industry clearly stands to benefit from cheaper technologies to obtain compressional and shear wave slowness data. Therefore, this paper delineates an alternative solution for the prediction of sonic log data by means of Machine Learning (ML). This study takes advantage of an adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM) ML techniques to predict compressional and shear wave slowness from drilling data only. In particular, the network is trained utilizing 2000 data points such as weight on bit (WOB), rate of penetration (ROP), standpipe pressure (SPP), torque (T), drill pipe rotation (RPM), and mud flow rate (GPM). Consequently, acoustic properties of the rock can be estimated solely from readily available parameters thereby saving both costs and time associated with sonic logs. The obtained results are promising and supportive of both ANFIS and SVM model as viable alternatives to obtain sonic data without the need for running sonic logs. The developed ANFIS model was able to predict compressional and shear wave slowness with correlation coefficients of 0.94 and 0.98 and average absolute percentage errors (AAPE) of 1.87% and 2.61%, respectively. Similarly, the SVM model predicted sonic logs with high accuracy yielding to correlation coefficients of more than 0.98 and AAPE of 0.74% and 0.84% for both compressional and shear logs, respectively. Once a network is trained, the approach naturally lends itself to be integrated as a real time service. This study outlines a novel and cost-effective solution to estimate rock compressional and shear-wave slowness solely from readily available drilling parameters. Importantly, the model has been verified for wells drilled in different formations with complex lithology substantiating the effectiveness of the approach.

Multiple Regression and Modified Faust Equation on Well Logging Data in Application to Seismic Procedures: Polish Outer Carpathians Case Study

An appropriate velocity model from well logs is a key issue in the processing and interpretation of seismic data. In a deep borehole located in the central part of the Polish Outer Carpathians, the sonic measurements were inadequate for seismic purposes due to the poor quality of data and gaps in the logging. Multiple regression (MR) and a modified Faust equation were proposed to model the velocity log. MR estimated the P-wave slowness as a dependent variable on the basis of sets of various logs as independent variables. The solutions were verified by the interval velocity from Check Shots (CS) and by the convergence of synthetic seismograms and the real seismic traces. MR proved to be an effective method when a set of other logs was available. The modified Faust method allowed computation of P-wave velocity based on the shallow resistivity logs, depth, and compaction factor. Faust coefficients were determined according to the lithology and stratigraphy divisions and were calibrated with the use of the velocity previously determined in the MR analysis. The modified Faust equation may be applied in nearby old wells with limited logging data, particularly with no sonic logs, where MR could not be successfully applied.

Electrofacies as a Tool for the Prediction of True Resistivity Using Advanced Statistical Methods—Case Study

Statistical analysis methods have been widely used in all industries. In well logs analyses, they have been used from the very beginning to predict petrophysical parameters such as permeability and porosity or to generate synthetic curves such as density or sonic logs. Initially, logs were generated as simple functions of other measurements. Then, as a result of the popularisation of algorithms such as the k-nearest neighbours (k-NN) or artificial neural networks (ANN), logs were created based on other logs. In this study, various industry and general scientific programmes were used for statistical data analysis, treating the well logs data as individual data sets, obtaining very convergent results. The methods developed for processing well logs data, such as Multi-Resolution Graph-Based Clustering (MRGBC), as well as algorithms commonly used in statistical analysis such as Kohonen self-organising maps (SOM), k-NN, and ANN were applied. The use of the aforementioned statis-tical methods allows for the electrofacies determination and prediction of an Rt log based on the other recorded well logs. Correct determination of Rt in resistivity measurements made with the Dual Laterolog tool in the conditions of the Groningen effect is often problematic. The applied calculation methods allow for the correct estimation of Rt in the tested well.

Sequence Stratigraphy and Depositional Environment of the Zubair Formation in Rumaila Oilfields, Southern Iraq: Microfacies and Geochemistry

In the Rumaila oilfields in southern Iraq, the Zubair Formation was deposited in a shallow environment as three main facies, delta plain, backshore, and delta front depositional conditions indicating a transition from delta front and delta plain to a highstand level due to the finning upward mode. The facies of the Zubair clasts show well-sorted quartz arenite sandstone, poorly sorted quartz arenite sandstone, clayey sandstone that has not been properly sorted, sandy shale, and shale lithofacies. The minor lithofacies were identified using well-logging methods (gamma ray, spontaneous potential and sonic logs) and petrography. The Zubair clasts are of transition environment that appears to be transported from freshwater and deposited in a marine environment forming many fourth-order cycles reflect sea level rise fluctuations and still-stand under tectonics developed the sequence stratigraphy. A misalignment between relative sea-level and sediment supply caused asymmetry sedimentary cycles. A shallower environment of shale-dominated rocks rich in organic matter and pyrite were exposed. The basinal shale of Ratawi at the Zubair bottom and the shallow carbonate of Shuaiba emplace on the Zubair represent the beginning of the delta build up (delta front and delta plain) to a highstand stage.