مجله ژئومکانیک نفت
سال چهارم شماره 2 (تابستان 1400)

Use of PDC bits in oil industry has been increased in recent years despite the fact that they are more expensive than roller cone bits. Therefore, developing a comprehensive rock-bit interaction model for this type of bits is too important. Previously, several mathematical models have been proposed for modeling rock-bit interaction. Most of these models (e.g. Nishimatsu’s model) do not consider the effects of in-situ stresses at all. Some other models assume that the values of rock failure angle at downhole and ambient conditions are equal. In fact, even though these models use in-situ stresses to calculate the cutting force, they do not consider in-situ stresses in analytical modeling. In this work, an analytical relation for rock failure angle including in-situ stresses is derived. Based on this relation, a novel analytical rock-bit interaction model to predict cutting force is presented. Applying field data on mentioned models results in improving rock failure angle and cutting force calculations for about 15-20%.

Geometry of fractures including orientation, spacing, aperture and etc. are the influential parameters on permeability in rocks. In order to investigate the effect of spacing and orientation of fractures on permeability in laboratory scale, selecting proper sample is essential in terms of physical and mechanical properties. Therefore, in this study, two types of samples (concrete and fibrous fiber) were selected. Then, physical and mechanical parameters, which consist of density, water absorption, porosity, wave velocity, uniaxial compressive strength and permeability, were measured. The test results showed that the concrete sample is not appropriate sample for studying effect of fracture parameters on permeability due to high porosity, water absorption, permeability and brittle behavior of sample. However, the sample of fibrous fiber not only possesses the favorable physical and mechanical properties but also is suitable to create the fractures with different spacing and orientations as well as the same aperture owing to flexibility and ductile behavior.

Hydraulic fracturing is one of the most important methods that has been used for several years to increase production, especially in low permeability fields. There are many two-dimensional and three-dimensional models to design hydraulic fracturing, which in this paper by using a combination of the Unified Fracture Design method and two-dimensional Khristianovich-Geertsma De klerk model, an optimum hydraulic fracturing design is modeled.In this paper, by making an initial guess for the amount of fracture permeability in different proppant mass, the parameters required to design hydraulic fracturing are obtained, and then by using graphs that connect fracture conductivity, fracture closure, and proppant concentration in fracture, fracture permeability will be updated. These graphs are different for different proppant types where in this article, the graph related to sand with mesh size 30/50 has been used.After calculating all the required parameters in the different proppant masses, is necessary to select the most suitable proppant mass, which two different ways can be used; in the first method, according to the values of some parameters such as injection rate, skin effect, surface injection pressure, efficiency of hydraulic fracturing operation and other parameters, the desired proppant mass is selected and in the second method the proppant mass corresponding to the desired injection rate is obtained and the design of the hydraulic fracturing is based on the intended injection rate. Finally, to ensure the accuracy of the results obtained by this method, a sensitivity test is performed which proves the validity of this method.

Determination of rock types is of special importance in the construction of static and dynamic models of hydrocarbon reservoirs. Estimating the properties of reservoir rocks increases the accuracy in predicting the amount of reservoir storage and its performance. Numerous models have been proposed by experts to determine the types of reservoir rocks. But most of the proposed models are based on conventional methods based on engineering and geology of carbonate reservoir rocks. Therefore, using a machine learning method to determine rock species in comparison with previous methods and comparing its efficiency and performance with other methods seems necessary. In this study, core and log data in maroon oil reservoir after preparation were match using Dynamic Time Series (DTW) technique for depth matching. The brain data were then clustered by the non-supervised machine learning method. The kernel data clustering process was also performed by conventional model-based methods such as flow zone index method (FZI) and Winland. Then, the clustering results were validated and compared with kmeans, FZI and Winland methods by having the lithology information of the logs. The kmeans method with a 93.5% accuracy criterion succeeded in performing the highest cluster resolution, which showed that the kmeans data-based machine learning method is a suitable alternative to conventional model-based methods for clustering rock typing.

Permeability is an important feature of oil and gas reservoirs that is difficult to predict. At present, experimental and regression models are used to predict permeability. On the other hand, increasing the accuracy of permeability prediction for points that do not have a core sample is of particular importance in analyzing reservoir behavior. In recent times, due to better predictability, machine learning algorithms have been used to predict permeability. In this study, a new group machine learning model for permeability prediction in oil and gas tanks is introduced. In this method, the input data is labeled using log lithology information and separated into a number of clusters and each cluster was modeled by machine learning algorithm. Unlike previous studies that worked independently on models, here we design a group model using augmented decision tree (ETR), decision tree (DTR) regression, and enhanced gradient (GBR) algorithms. And petrophysical data, we were able to dramatically improve the accuracy of the prediction as well as the mean square error and predict the permeability with 99.82% accuracy. The results showed that group models have a great effect on improving the accuracy of permeability prediction compared to individual models and also the separation of samples based on lithology information was a reason to optimize the Trojan estimate compared to previous studies.

Drilling fluid plays an important role during the drilling operation of a well and can lead to a lot of problems or cure different challenges. Loss circulation is one of these challenges, which can increase non-productive time and operation costs, dramatically. The key solution for solving this problem is to increase the pressure-bearing capacity of the formation and to strengthen the wellbore with the help of drilling fluid. The purpose of this study is to design the "Wellbore Strengthening Evaluation" apparatus to simulate the formation fracturing process and investigate the LCM effects on the improvement of mud properties and rock fracture gradient. Therefore, in this paper, the cylindrical concrete cores are used as rock samples and Water-based bentonite drilling mud containing two different types of LCM are considered as drilling fluid. The results of experiments show that the addition of different LCMs to the drilling fluid leads to an increase in the formation breakdown pressure (FBP) and fracture reopening pressure (FRP) to 33 and 72%, respectively. Therefore, before drilling a formation with the possibility of loss circulation, it is an effective practice to add LCM particles in the drilling fluid to increase the fracture pressure of the formation.