scholarly journals Derivation and application of mathematical model for well test analysis with variable skin factor in hydrocarbon reservoirs

AIP Advances ◽  
2016 ◽  
Vol 6 (6) ◽  
pp. 065324 ◽  
Author(s):  
Pengcheng Liu ◽  
Wenhui Li ◽  
Jing Xia ◽  
Yuwei Jiao ◽  
Aifang Bie
Keyword(s):  
Mathematical Model ◽  
Hydrocarbon Reservoirs ◽  
Well Test ◽  
Well Test Analysis ◽  
Test Analysis ◽  
Skin Factor

scholarly journals Well Test and Short Term Multiple Rate Flow Tests Analyses to Successfully Hydraulic Fracturing Program of Block X

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Muhammad Dimas Adiguna ◽  
Muhammad Taufiq Fathaddin ◽  
Hari Karyadi Oetomo
Keyword(s):  
Secondary Data ◽  
Test Method ◽  
Well Test ◽  
Short Term ◽  
Well Test Analysis ◽  
Test Analysis ◽  
Pressure Buildup ◽  
Skin Factor ◽  
Oil Flow ◽  
Rate Test

<p>Well test analysis was conducted to determine the characteristics of reservoir rocks. From the well test analysis it is obtained information such as permeability and skin factor. The skin factor is a quantity indicating the presence of disturbance in the formation as a result of drilling operations, production operations, perforating casing, gravel pack installation, remedial well work, acidizing operation, and hydraulic fracture operation. The objective of this research is to determine the relationship of multi rate test method of Jones, Blount, and Glaze and the comparison result among pressure buildup test and pressure drawdown test analyses, using Kappa software or manually calculation. Therefore, in this paper will study the method of Jones, Blount, and Glaze and the well test analyses to determine further work of the wells on block X. The data used in this paper is secondary data, namely the results of well test from three wells.Applying drawdown test analysis of A, Y, and Z wells yield skin factor values of 3.37; 27.10; and -1.39. Where in buildup pressure Horner method analysis of A, Y, and Z wells yield skin factor values of 16.10; 11.18; and -2.07. In the method of type curve derivatives the drawdown analysis of A, Y, and Z wells yield skin factor values of 7.04; 11.18; and 4.20. The analysis of pressure buildup, of A, Y, and Z wells yield skin factor value of 25.11; 14.47; and 1.93. In the analysis using <br /> Kappa software of A, Y, and Z wells yield skin factor values of 5.56; 10.2; and 2.00. The skin results of these wells indicate the formation damages. The Short Term Multiple Rate Flow Tests analysis using Jones, Blount, and Glaze method from the plots of Δp/q versus oil flow rate (q) are b’ high and b’/b low. These indicate that the three wells are encountering formation damages. The Jones, Blount, and Glaze method as well as the pressure buildup and pressure drawdown test analyses in block X indicate that these wells require to be stimulated.</p>

scholarly journals A mathematical model and semi-analytical solution for transient pressure of vertical fracture with varying conductivity in three crossflow rectangular layers

2018 ◽  
Vol 37 (1) ◽  
pp. 230-250 ◽  
Author(s):  
Jie Liu ◽  
Pengcheng Liu ◽  
Shunming Li ◽  
Xiaodong Wang
Keyword(s):  
Mathematical Model ◽  
Analytical Solution ◽  
Flow Characteristics ◽  
Well Test ◽  
Transient Pressure ◽  
Well Test Analysis ◽  
Test Analysis ◽  
Fracture Conductivity ◽  
Vertical Fracture ◽  
Flow Phase

This paper first describes a mathematical model of a vertical fracture with constant conductivity in three crossflow rectangular layers. Then, three forms of vertical fracture (linear, logarithmic, and exponential variations) with varying conductivity are introduced to this mathematical model. A novel mathematical model and its semi-analytical solution of a vertical fracture with varying conductivity intercepting a three-separate-layered crossflow reservoir is developed and executed. Results show that the transient pressures are divided into three stages: the linear-flow phase, the medium unsteady-flow stage, and the later pseudo-steady-flow phase. The parameters of the fracture, reservoir, and the multi-permeability medium directly influence the direction, transition, and shape of the transient pressure. Meanwhile, the fracture conductivity is higher near the well bottom and is smaller at the tip of the fracture for the varying conductivity. Therefore, there are many more differences between varying conductivity and constant conductivity. Varying conductivity can correctly reflect the flow characteristics of a vertical fractured well during well-test analysis.

Reservoir Characterization Constrained to Well-Test Data: A Field Example

2000 ◽  
Vol 3 (04) ◽  
pp. 325-334 ◽  
Author(s):  
J.L. Landa ◽  
R.N. Horne ◽  
M.M. Kamal ◽  
C.D. Jenkins
Keyword(s):  
Mathematical Model ◽  
Test Data ◽  
Pressure Response ◽  
Well Testing ◽  
Pressure Measurements ◽  
Well Test ◽  
Well Test Analysis ◽  
Test Analysis ◽  
Reservoir Description ◽  
Different Sources

Summary In this paper we present a method to integrate well test, production, shut-in pressure, log, core, and geological data to obtain a reservoir description for the Pagerungan field, offshore Indonesia. The method computes spatial distributions of permeability and porosity and generates a pressure response for comparison to field data. This technique produced a good match with well-test data from three wells and seven shut-in pressures. The permeability and porosity distributions also provide a reasonable explanation of the observed effects of a nearby aquifer on individual wells. As a final step, the method is compared to an alternate technique (object modeling) that models the reservoir as a two-dimensional channel. Introduction The Pagerungan field has been under commercial production since 1994. This field was chosen to test a method of integrating dynamic well data and reservoir description data because the reservoir has only produced single phase gas, one zone in the reservoir is responsible for most of the production, and good quality well-test, core, and log data are available for most wells. The method that was used to perform the inversion of the spatial distribution of permeability and porosity uses a parameter estimation technique that calculates the gradients of the calculated reservoir pressure response with respect to the permeability and porosity in each of the cells of a reservoir simulation grid. The method is a derivative of the gradient simulator1 approach and is described in Appendices A and B. The objective is to find sets of distributions of permeability and porosity such that the calculated response of the reservoir closely matches the pressure measurements. In addition, the distributions of permeability and porosity must satisfy certain constraints given by the geological model and by other information known about the reservoir. Statement of Theory and Definitions The process of obtaining a reservoir description involves using a great amount of data from different sources. It is generally agreed that a reservoir description will be more complete and reliable when it is the outcome of a process that can use the maximum possible number of data from different sources. This is usually referred to in the literature as "data Integration." Reservoir data can be classified as "static" or "dynamic" depending on their connection to the movement or flow of fluids in the reservoir. Data that have originated from geology, logs, core analysis, seismic and geostatistics can be generally classified as static; whereas the information originating from well testing and the production performance of the reservoir can be classified as dynamic. So far, most of the success in data integration has been obtained with static information. Remarkably, it has not yet become common to completely or systematically integrate dynamic data with static data. A number of researchers,2–5 are studying this problem at present. This work represents one step in that direction. Well Testing as a Tool for Reservoir Description. Traditional well-test analysis provides good insight into the average properties of the reservoir in the vicinity of a well. Well testing can also identify the major features of relatively simple reservoirs, such as faults, fractures, double porosity, channels, pinchouts, etc. in the near well area. The difficulties with this approach begin when it is necessary to use the well-test data on a larger scale, such as in the context of obtaining a reservoir description. One of the main reasons for these difficulties is that traditional well-test analysis handles transient pressure data collected at a single well at a time, and is restricted to a small time range. As a result, traditional well-test analysis does not make use of "pressure" events separated in historical time. The use of several single and multiple well tests to describe reservoir heterogeneity has been reported in the literature,6 however, this approach is not applied commonly because of the extensive efforts needed to obtain a reservoir description. The method presented in this paper uses a numerical model of the reservoir to overcome these shortcomings. It will be shown that pressure transients can be used effectively to infer reservoir properties at the scale of reservoir description. Well-test data, both complete tests and occasional spot pressure measurements, will be used to this effect. The well-test information allows us to infer properties close to the wells and, when combined with the shut-in pressures (spot pressure), boundary information and permeability-porosity correlations, provides the larger scale description. General Description of the Method The proposed method is similar to other parameter estimation methods and thus consists of the following major items: the mathematical model, the objective function and the minimization algorithm. Mathematical Model. Because of the complexity of the reservoir description, the reservoir response must be computed numerically. Therefore, the pressure response is found using a numerical simulator. The reservoir is discretized into blocks. The objective is to find a suitable permeability-porosity distribution so that values of these parameters can be assigned to each of the blocks.

scholarly journals Analysis of Pressure and Production Transient Characteristics of Composite Reservoir with Moving Boundary

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 34
Author(s):  
Zhixue Sun ◽  
Xugang Yang ◽  
Yanxin Jin ◽  
Shubin Shi ◽  
Minglu Wu
Keyword(s):  
Mathematical Model ◽  
Oil Recovery ◽  
Moving Boundary ◽  
Initial Boundary ◽  
Well Test ◽  
Well Test Analysis ◽  
Test Analysis ◽  
Wellbore Storage ◽  
Boundary Velocity ◽  
The Mathematical Model

The mathematical model of composite reservoir has been widely used in well test analysis. In the process of oil recovery, due to the injection or replacement of the displacement agent, the model boundary can be moved. At present, the mathematical model of a composite reservoir with a moving boundary is less frequently studied and cannot meet industrial demand. In this paper, a mathematical model of a composite reservoir with a moving boundary is developed, with consideration of wellbore storage and skin effects. The characteristics of pressure transient in moving boundary composite reservoir are studied, and the influences of parameters, such as initial boundary radius, moving boundary velocity, skin factor, wellbore storage coefficient, diffusion coefficient ratio, and mobility ratio on pressure and production, are analyzed. The moving boundary effects are noticeable mainly in the middle and late production stages. The proposed model provides a novel theoretical basis for well test analysis in these types of reservoirs.

Connecting Data, Model and Process to Streamline and Enhance Single-Point Production Well Test Validation

2021 ◽  
Author(s):  
Mohamad Mustaqim Mokhlis ◽  
Nurdini Alya Hazali ◽  
Muhammad Firdaus Hassan ◽  
Mohd Hafiz Hashim ◽  
Afzan Nizam Jamaludin ◽  
...  
Keyword(s):  
Test Data ◽  
Single Point ◽  
Database Systems ◽  
Test Validation ◽  
Well Test ◽  
Production Well ◽  
Well Test Analysis ◽  
Test Analysis ◽  
Digital Ecosystem ◽  
Well Model

Abstract In this paper we will present a process streamlined for well-test validation that involves data integration between different database systems, incorporated with well models, and how the process can leverage real-time data to present a full scope of well-test analysis to enhance the capability for assessing well-test performance. The workflow process demonstrates an intuitive and effective way for analyzing and validating a production well test via an interactive digital visualization. This approach has elevated the quality and integrity of the well-test data, as well as improved the process cycle efficiency that complements the field surveillance engineers to keep track of well-test compliance guidelines through efficient well-test tracking in the digital interface. The workflow process involves five primary steps, which all are conducted via a digital platform: Well Test Compliance: Planning and executing the well test Data management and integration Well Test Analysis and Validation: Verification of the well test through historical trending, stability period checks, and well model analysis Model validation: Correcting the well test and calibrating the well model before finalizing the validity of the well test Well Test Re-testing: Submitting the rejected well test for retesting and final step Integrating with corporate database system for production allocation This business process brings improvement to the quality of the well test, which subsequently lifts the petroleum engineers’ confidence level to analyze well performance and deliver accurate well-production forecasting. A well-test validation workflow in a digital ecosystem helps to streamline the flow of data and system integration, as well as the way engineers assess and validate well-test data, which results in minimizing errors and increases overall work efficiency.

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