Calculating The Pseudo-Skin Factor Due To Partial Well Completion

1986 ◽  
Vol 25 (05) ◽  
Author(s):  
J. Vrbik
Keyword(s):  
2006 ◽  
Vol 9 (01) ◽  
pp. 61-76 ◽  
Author(s):  
Turhan Yildiz

Summary In this study, the available methods and software to predict the well productivity and total skin factor in fully perforated vertical wells have been reviewed. The methods have been compared against the experimental data obtained on an electrolytic apparatus, and their accuracy has been investigated. It has been observed that the 3D semianalytical model, SPAN 6.0 software, and the simple hybrid model described in this paper replicate the experimental results very well. On the other hand, the results estimated from the McLeod method and the Karakas-Tariq method substantially deviate from the experimental data; hence, these models/methods should be used with caution. The literature hosts many equations to predict the total skin factor in partially perforated vertical wells. Some of the available models have been tested against the results from the 3D semianalytical model. It has been shown that total skin-factor equations based on the summation of individual components do not work. The 3D semianalytical model has been modified to build an approximate model for fully and partially perforated inclined wells in isotropic formations. Additionally, a simple hybrid model to compute total skin factor in perforated inclined wells has been presented. The hybrid model for perforated inclined wells agrees well with the approximate 3D model. Some of the available models to calculate total skin factor in perforated inclined wells have been compared to the approximate 3D model, and their accuracy has been discussed. Finally, a simple model to predict total skin factors in perforated horizontal wells has been developed. The application using the simple model has demonstrated that a combination of long wellbore length and perforations bypassing the damaged zone could overcome the destructive effect of severe formation damage around the wellbore. Introduction The long-term productivity of oil and gas wells is influenced by many factors. Among these factors are petrophysical properties, fluid properties, degree of formation damage and/or stimulation, well geometry, well completions, number of fluid phases, and flow-velocity type. To isolate and identify the effect of any single parameter on the well performance, a sensitivity study on the parameter of interest is conducted, and the results are compared to a reference base case of an ideal vertical open hole. In the base case, the ideal vertical open hole produces single-phase fluid, the fluid flow obeys Darcy's law, and the formation is neither stimulated nor damaged. The influence of the individual parameters not considered in the base case is quantified in terms of skin factor. Oil and gas wells may have permeability reduction around the wellbore caused by invasion by drilling mud, cement, solids, and completion fluids. This is generally referred to as formation damage. Formation damage around the wellbore causes additional pressure drop. On the other hand, stimulation operations such as acidizing may decrease the pressure drop in the near-wellbore region by improving the formation permeability around the wellbore. The impact of permeability impairment/improvement around the wellbore caused by drilling, production, and acidizing operations is quantified in terms of mechanical skin factor. The fluid flow in the near-wellbore region is also influenced by well-completion type. Openhole completion yields a local flow pattern that is radial around the wellbore and normal to the well trajectory. However, in some cases, openhole completion may not be desirable. Different types of well completion may be needed to control/isolate fluid entry into the wellbore, to avoid gas/water coning, and to minimize sand production. Besides the openhole completion, wells may be partially or selectively completed with perforations, slotted liners, gravel packs, screens, and zonal-isolation devices. Also, wells with low productivity may need to be hydraulically fractured. In completed wells, the flow pattern around the wellbore is distorted. Completions result in additional fluid convergence and divergence in the near-wellbore region. For example, partial penetration creates a 2D flow field in the formation. On the other hand, a perforated well experiences 3D flow converging around perforation tunnels. Compared to an ideal open hole, the wells with completions are subject to additional pressure gain/loss in the near-wellbore region. The additional pressure change caused by well completion is quantified in terms of completion pseudoskin factor. Well performance is naturally influenced by the geometry of the well itself. Based on their geometrical shape, wells may be classified as vertical, inclined, horizontal, undulating, and multibranched. In the literature, the reference well geometry has been that of a fully penetrating vertical open hole. Historically, the differences in the productivity of vertical openhole and other well geometries have also been formulated in terms of pseudoskin factor. However, when it comes to the assessment of completion effects on well productivity, rather than comparing the given completed nonvertical well to an ideal vertical open hole, it may be more appropriate to work with the considered well geometry only and compare the completed and openhole cases of the same well geometry. For this reason, the term geometrical pseudoskin factor is proposed to quantify the differences between the productivities of vertical wells and other well geometries. Multiphase flow in the formation may evolve because of gas/water coning around the wellbore, gas evaporation from the liquid-hydrocarbon phase, and liquid dropout from gas condensate. Compared to single-phase fluid flow, multiphase flow in the formation creates an additional pressure drop because of the relative permeability effect. If multiphase flow is intensified in the near-wellbore region, only then may the impact of multiphase flow be formulated in terms of multiphase pseudoskin factor.


1985 ◽  
Vol 25 (01) ◽  
pp. 125-131 ◽  
Author(s):  
A.S. Odeh

Abstract Scaling factors for the proper application and interpretation of field-determined skin effect and pressure buildup values for use in simulators are derived. Reservoir engineering calculations for the actual well are based on a continuous physical system and the total effective formation thickness. For use with a simulator, the system is discretized, and the cell thickness replaces the total thickness. The scaling factors are to correct for the differences between the two systems. Without the scaling factors, the well inflow equations used in the simulators would calculate an erroneous pressure drop component as a result of the physical skin and the nondarcy flow effect. In the case of pressure values, an equation is derived that gives the buildup time, At, when the field-measured wellbore pressure becomes equal to the wellblock pressure in a three-dimensional simulator. This is important for history matching. This paper shows that the pressure-At relation is strongly coupled to the skin scaling factor. Introduction Reservoir simulation calculations consist mainly, of two parts:(1) the fluid saturation and pressure distribution and parts:the fluid saturation and pressure distribution andthe well inflow. The fluid saturation and pressure distribution result from the solution of the nonlinear partial differential equations that express the mass balance partial differential equations that express the mass balance for oil, water, and gas. Most of the research on reservoir modeling has been concerned with the solution of these equations, and significant progress has been achieved. Compared with this, the treatment of the well is still in its infancy. This is disconcerting since the well calculations are critical to the matching and prediction phases of simulation. In reservoir engineering, the well inflow calculations have reached a high degree of sophistication. The effects of the well completion, restricted entry to flow, noncircular drainage area, and nondarcy flow can be accounted for. The treatment of these factors relies on three basic assumptions:the physical model is continuous-i.e., no discretization is involved as it is in the numerical model,the thickness used in the calculations is the total effective thickness of the formation, andthe permeability is the integrated average of the permeability is the integrated average of the permeability values in the drainage area of the well. This is permeability values in the drainage area of the well. This is normally obtained from flow test analyses. In reservoir simulators, all three basic assumptions are violated. The reservoir is discretized; the thickness used in the inflow equation is the thickness of the cell, which is usually much less than the formation thickness; and the permeability of the cell with a well is different from the average permeability in the majority of cases. This introduces a permeability in the majority of cases. This introduces a scaling problem. If the field-determined well inflow parameters are not scaled properly for use in the parameters are not scaled properly for use in the simulators, the simulation results may not reflect the true well behavior. Furthermore, the pressure values used for matching purposes may be the wrong values. In this paper, the scaling of the skin factor and the problems associated with it are considered. A scaling factor problems associated with it are considered. A scaling factor that gives an acceptable match between the field pressure drop caused by skin and the model-calculated value is determined. Also, an equation that gives the buildup time, At, when the well pressure becomes equal to the cell pressure is derived. The equation accounts for pressure is derived. The equation accounts for three-dimensional (3D) flow and the completion of the well. The implication of using the incorrect At during the history matching phase of a simulation study is analyzed. Skin Effect Consideration The difference between the discretized mathematical model and the continuous physical system is most apparent in the treatment of the skin factor in the inflow equations. The skin factor is an indication of the efficiency of the well completion. The skin concept was introduced to the petroleum industry by Hurst and van Everdingen. petroleum industry by Hurst and van Everdingen. They considered the skin to result from a permeability change in the vicinity of the wellbore. The skin concept was extended by Brons and Marting and by Odeh to account for restricted entry and by Ramey to account for nondarcy flow. The normal procedure for calculating the skin effect is based on the net effective thickness of the formation. In the classical skin determination from buildup data, it is calculated by .....................................(1) where S T == SA + S R, and k is obtained from the flow test analysis. The pressure drop caused by skin, is .....................................(2) SPEJ


Author(s):  
M.M. Khasanov ◽  
◽  
K.E. Lezhnev ◽  
V.D. Pashkin ◽  
A.P. Roshchektaev ◽  
...  
Keyword(s):  

2020 ◽  
Vol 7 ◽  
pp. 33-35
Author(s):  
V.А. Iktisanov ◽  
◽  
N.Kh. Musabirova ◽  
А.V. Baygushev ◽  
М.Kh. Bilalov ◽  
...  

2019 ◽  
pp. 36-38
Author(s):  
M.V. Zaitsev ◽  
◽  
N.N. Mikhailov ◽  
Keyword(s):  

2019 ◽  
Vol 11 (19) ◽  
pp. 5283 ◽  
Author(s):  
Gowida ◽  
Moussa ◽  
Elkatatny ◽  
Ali

Rock mechanical properties play a key role in the optimization process of engineering practices in the oil and gas industry so that better field development decisions can be made. Estimation of these properties is central in well placement, drilling programs, and well completion design. The elastic behavior of rocks can be studied by determining two main parameters: Young’s modulus and Poisson’s ratio. Accurate determination of the Poisson’s ratio helps to estimate the in-situ horizontal stresses and in turn, avoid many critical problems which interrupt drilling operations, such as pipe sticking and wellbore instability issues. Accurate Poisson’s ratio values can be experimentally determined using retrieved core samples under simulated in-situ downhole conditions. However, this technique is time-consuming and economically ineffective, requiring the development of a more effective technique. This study has developed a new generalized model to estimate static Poisson’s ratio values of sandstone rocks using a supervised artificial neural network (ANN). The developed ANN model uses well log data such as bulk density and sonic log as the input parameters to target static Poisson’s ratio values as outputs. Subsequently, the developed ANN model was transformed into a more practical and easier to use white-box mode using an ANN-based empirical equation. Core data (692 data points) and their corresponding petrophysical data were used to train and test the ANN model. The self-adaptive differential evolution (SADE) algorithm was used to fine-tune the parameters of the ANN model to obtain the most accurate results in terms of the highest correlation coefficient (R) and the lowest mean absolute percentage error (MAPE). The results obtained from the optimized ANN model show an excellent agreement with the laboratory measured static Poisson’s ratio, confirming the high accuracy of the developed model. A comparison of the developed ANN-based empirical correlation with the previously developed approaches demonstrates the superiority of the developed correlation in predicting static Poisson’s ratio values with the highest R and the lowest MAPE. The developed correlation performs in a manner far superior to other approaches when validated against unseen field data. The developed ANN-based mathematical model can be used as a robust tool to estimate static Poisson’s ratio without the need to run the ANN model.


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