Simultaneous Multifracture Treatments: Fully Coupled Fluid Flow and Fracture Mechanics for Horizontal Wells

Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks

A review on hydraulic fracturing of unconventional reservoir

Many studies have shown that artificial neural networks (ANNs) are useful for predicting the unconfined compressive strength (UCS) of rocks. However, ANNs do have some deficiencies: they can get trapped in local minima and they have a slow learning rate. It is widely accepted that optimization algorithms such as particle swarm optimization (PSO) can improve ANN performance. This study investigated the application of a hybrid PSO-based ANN model to the prediction of rock UCS. To prepare a dataset for the predictive model, extensive laboratory tests (i.e., 160 tests in total) were conducted on 40 soft rock sample sets (mostly shale) presenting various weathering grades that were obtained from different excavation sites in Johor, Malaysia. The laboratory tests included the UCS test and other basic rock index tests (the Brazilian tensile strength test, point load index test, and ultrasonic test). When developing the predictive model of UCS, the results of the basic rock tests as well as the bulk densities of the samples were used as input parameters, while the UCS was set as the output of the predictive model. The value account for (VAF), root mean squared error (RMSE), and adjusted R
 2 (coefficient of determination) were utilized to check the performances of the predictive models. The high performance indices of the proposed model highlight the superiority of the PSO-based ANN model for UCS prediction.

Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach

Use of machine learning and data analytics to increase drilling efficiency for nearby wells

Intelligent Prediction of Differential Pipe Sticking by Support Vector Machine Compared With Conventional Artificial Neural Networks: An Example of Iranian Offshore Oil Fields

Hydraulic fracturing of a naturally fractured reservoir is a challenge for petroleum industry, as fractures can have complex growth patterns when propagating in systems of natural fractures that leads to significant diversion of hydraulic fracture paths due to intersection with natural fractures which causes difficulties in proppant transport. In this study, an eXtended Finite Element Method (XFEM) model has been developed to account for hydraulic fracture propagation and interaction with natural fracture in naturally fractured reservoirs including fractures intersection criteria into the model. It is assumed that fractures are propagating in an elastic medium under plane strain and quasi-static conditions. The results indicate that hydraulic fracture diversion before and after intersecting with natural fracture is strongly controlled by the in-situ horizontal differential stress and the orientation of the natural fractures as well as hydraulic fracture net pressure. It is observed that hydraulic fracture net pressure increase leads to decreasing induced fracture diversion and in-situ horizontal differential stress decrease results in increasing induced fracture diversion before intersecting with natural fracture. In addition, potential debonding of sealed natural fracture in the near-tip region of a propagating hydraulic fracture before fractures intersection has been modeled which is one of the phenomena that has been rarely taken into account, as debonding of natural fracture before fractures intersection is of great importance that may lead to diverting the induced fracture into double-deflection in natural fracture and can explain hydraulic fracture behaviors due to interaction with natural fracture at different conditions. Also, it’s been observed that at low angles of approach with low to high differential stress, the induced hydraulic fracture opens the natural fracture while at high to medium angles of approach, natural fracture opening and crossing both are observed depending on the differential stress. Comparison of the numerical and experimental studies results has shown good agreement.

A New Approach for Numerical Modeling of Hydraulic Fracture Propagation in Naturally Fractured Reservoirs

Real-time Prediction of Rate of Penetration During Drilling Operation In Oil And Gas Wells

Accurate prediction of pore pressures has become almost essential to exploration, completion and drilling wells with higher-than-normal pore pressures. There are some limitations with the current existing pore pressure estimation methods, as they are based on empirical relations and constants which can differ from basin to basin. In this study, a feed-forward network with back-propagation algorithm and a generalised regression neural network have been developed to predict pore pressure gradient in Asmari reservoir in one of oilfields in Iran. To show the accuracy of the method, the results of the developed models have been compared with Eaton’s method.

Real-time prediction of pore pressure gradient through an artificial intelligence approach: a case study from one of middle east oil fields

Lost circulation is one of the most troublesome and costly problems in drilling industry. This phenomenon can lead to some serious problems such as wellbore instability, differential pipe sticking and blow out. Meanwhile, it is possible to mitigate the risk of lost circulation by carrying out geomechanical analysis and real-time prediction. Artificial neural network (ANN) is an efficient tool to predict the events due to the effective parameters. Generally, there are various factors including geomechanical and non-geomechanical parameters that affect lost circulation. In this paper, two ANNs have been developed to predict the lost circulation in naturally fractured formations. Firstly, an ANN has been designed without considering geomechanical parameters as inputs. Then another ANN has been developed by considering both the geomechanical and non-geomechanical parameters. Finally, two ANNs have been compared to investigate the geomechanical parameters effect on lost circulation. In both the models, princip...

Reza Keshavarzi

Papers

Intelligent Prediction of Differential Pipe Sticking by Support Vector Machine Compared With Conventional Artificial Neural Networks: An Example of Iranian Offshore Oil Fields

A New Approach for Numerical Modeling of Hydraulic Fracture Propagation in Naturally Fractured Reservoirs

Real-time Prediction of Rate of Penetration During Drilling Operation In Oil And Gas Wells

Real-time prediction of pore pressure gradient through an artificial intelligence approach: a case study from one of middle east oil fields

Artificial neural network-based prediction and geomechanical analysis of lost circulation in naturally fractured reservoirs: a case study