Predicting density log from well log using machine learning techniques and heuristic optimization algorithm:A comparative study

In the petroleum industry,the analysis of petrophysical parameters is critical for efficient reservoir management,production optimization,development strategies,and accurate hydrocarbon reserve estimations.Over recent years,the integration of machine learning methodologies has revolutionized the field,addressing challenges in geology,geophysics,and petroleum engineering,even when confronted with limited or imperfect data.This study focuses on the prediction of density logs,a pivotal factor in evaluating reservoir hydrocarbon volumes.It is important to note that during well logging operations,log data for specific depths of interest may be missing or incorrect,presenting a significant challenge.To tackle this issue,we employed the Adaptive Neuro-Fuzzy Inference System(ANFIS)and Artificial Neural Networks(ANN)in combination with advanced optimization algorithms,including Particle Swarm Optimization(PSO),Imperialist Competitive Algorithms(ICA),and Genetic Algorithms(GA).These methods exhibit promising performance in predicting density logs from gamma-ray,neutron,sonic,and photoelectric log data.Remarkably,our results highlight that the Genetic Algorithms-based Artificial Neural Network(GA-ANN)approach outperforms all other methods,achieving an impressive Mean Squared Error(MSE)of 0.0013.In comparison,ANFIS records an MSE of 0.0015,ICA-ANN 0.0090,PSO-ANN 0.0093,and ANN 0.0183.

Clay Minerals Properties as Downhole Formation Pressure Indicator

The successful estimation of formation pressures （or formation pore gradient） is fundamental and the basis for many engineering works including drilling and oilfield development planning. Common log data are used for formation pressure calculation. Modern techniques for pressure prediction have several disadvantages, notably, incorrect account of the downhole nonsteady thermal field and clay mineral composition. We propose a way to overcome listed shortcomings： a technique for thermal field proper account while formation pressure estimation and a petrophysical model, which reflects relationships between clay minerals composition and rock properties, derived from log data.

Investigation of production depletion rate effect on the ne ar-wellbore stresses in the two Iranian southwest oilfields|

Effect of depletion rate on wellbore stability due to production of oil from hydrocarbon-bearing reser-voirs is important and can result in a reduction of the reservoir pore pressure.A reduction in the reservoir pressure,in tum,results in a change in the stresses acting within the reservoir.If these changes are not well analyzed and applied in future calculations,we may be unsuccessful in some cases such as drilling infill wells or extended wells,forecast of sand production and optimum flow rate,preventing of casing collapse,etc.Therefore,knowing the stresses around the well at every stage of production is fundamental to the next calculations.In this paper,we recognized these changes in near-wellbore stresses using data from logging(image log,dipole sonic,full set log,mud log),production data,infor-mation from kick,blow out,loss etc.and determined it using Techlog software and theoretical calcu-lations.Three wells of two huge oilfields of sw Iran were investigated after depletion or production for long years.In each well,state of stresses were calculated in cases of safe drilling mud weight using log data and theoretical equations.At the end effect of depletion rate on wellbore stability was investigated in these three wells.

Fracture permeability estimation utilizing conventional well logs and flow zone indicator

Characteristics of the natural open fractures on the oil and gas reservoirs is crucial in drilling and production planning. Direct methods of fractures studies such as core analysis and image log interpretation are usually not performed in all drilled wells in a field. Therefore, in absence of these data, the indirect methods can play an important role. In this study, an integrated algorithm is introduced to identify the fractures and estimate its permeability employing conventional well logs. First, open fractures were identified and their properties including density, aperture, porosity and permeability were estimated using FMI log. Subsequently, the fracture index log (FR_Index) was estimated utilizing conventional logs including density, micro-resistivity, sonic (compressional, shear and stoneley slownesses), and caliper logs. After that, the fracture index permeability was estimated by improving the FZI permeability equation. The coherence coefficient between two estimated fracture permeability logs is 0.66. A good correlation is observed on the high permeability zones, but the lower correlation on the low permeability zones. It is notified that, in the high fracture permeability zones, the conventional logs are heavily impacted by fracture permeability. However, due to lower vertical resolution of conventional logs compared with the image logs, the conventional logs are less influenced by less dense fracture zones. However, this algorithm can be used with acceptable accuracy in all uncored and image log wells.

Investigation of trend between porosity and drilling parameters in one of the Iranian undeveloped major gas fields

Porosity is one of the main parameters that should be considered when a gas or an oil field is assessed.Hence,the availability of porosity is crucial in any rock and formation analysis.However,this parameter is not always available and direct measurement is scarce and usually is confined to appraisal phase of a field.Therefore,in this study it is aimed to find a relationship in order to estimate porosity in afterward phases.The data of 5 appraisal wells of an undeveloped gas field has been gathered to perform this purpose.The full set wireline logging data and drilling data were available.The data inherit high level of fluctuation which is normal with respect to real direct measured data.The smoothed curving was utilized and as a result,the fluctuation was diminished considerably and the feasibility of carrying out the investigation was being established,accordingly.To normalize and gather drilling data in a parameter in order to have a better view and investigation,sigmalog method was employed.It was observed that there is a clear exponential relationship between porosity and drilling data and curve fitting was utilized to conduct a numerical relationship.The curve fitting was established with R^(2)=0.83 and a trustworthy fitting resulted.The results demonstrate the possibility of porosity estimation by using drilling data.Furthermore,a numerical relationship is given for a specific field.The porosity estimation can lead to better analysis of newly drilled rocks and formations leading to more efficient field development strategies.

Reservoir rock properties estimation based on conventional and NMR log data using ANN-Cuckoo:A case study in one of super fields in Iran southwest

This work highlights the application of Artificial Neural Networks optimized by Cuckoo optimization algorithm for predictions of NMR log parameters including porosity and permeability by using field log data.The NMR logging data have some highly vital privileges over conventional ones.The measured porosity is independent from bearer pore fluid and is effective porosity not total.Moreover,the permeability achieved by exact measurement and calculation considering clay content and pore fluid type.Therefore availability of the NMR data brings a great leverage in understanding the reservoir properties and also perfectly modelling the reservoir.Therefore,achieving NMR logging data by a model fed by a far inferior and less costly conventional logging data is a great privilege.The input parameters of model were neutron porosity(NPHI),sonic transit time(DT),bulk density(RHOB)and electrical resistivity(RT).The outputs of model were also permeability and porosity values.The structure developed model was build and trained by using train data.Graphical and statistical validation of results showed that the developed model is effective in prediction of field NMR log data.Outcomes show great possibility of using conventional logging data be used in order to reach the precious NMR logging data without any unnecessary costly tests for a reservoir.Moreover,the considerable accuracy of newly ANN-Cuckoo method also demonstrated.This study can be an illuminator in areas of reservoir engineering and modelling studies were presence of accurate data must be essential.

Investigation of quality factor frequency content in vertical seismic profile for gas reservoirs

Since long ago, indirect study of the underground layers properties has been interesting to geologists.One method for this study was seismography which gained great interest besides other tools due to thedifferent identity of waves and energy attraction phenomena in different layers. Vertical seismic profiling(VSP) is considered as a valuable method in oil and gas exploration. This method is used to estimate therock properties in a well. In seismic operations elastic waves are sent down to the underground. Part ofthe waves’ energy is reflected after passing through the earth layers and are received by receivers on theground level. The received data determine the situation of the underneath layers after being processed,and one of the most important applications of seismic data is in the oil and gas exploration field. Qualityfactor is one of the most important seismic detectors that shows itself apparently in VSP data results. Themost substantial purpose of this study is to investigate the frequency content of the quality factor.