Study of Homogeneous Reservoir Pressure Inversion Model Based on Permeability Mechanics and Interpretation Software Design

Well test analysis is required during the extraction of oil and gas wells. The information on formation parameters can be inverted by measuring the change in wellbore pressure at production start-up or after well shutdown. In order to calculate the characteristic parameters of the well, this paper creates a well test interpretation model for homogeneous reservoirs based on the theory of seepage mechanics, uses the Stehfest–Laplace inversion numerical inversion algorithm, and builds the Gringarten–Bourdet logarithmic curves model. The model can be used to evaluate the homogeneous reservoir. We use this model to design the pressure inversion interpretation software to implement a pressure inversion method based on permeability mechanics theory by using computer. The software can obtain the reservoir characteristic parameters such as permeability ( K ), skin coefficient ( S ), and wellbore storage coefficient ( C ). The homogeneous formation Gringarten–Bourdet curves data are available at https://github.com/JXLiaoHIT/Study-of-homogeneous-reservoir-pressure-inversion-model.

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

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.

Deconvolution well test analysis applied to a long-term data set of the Waiwera geothermal reservoir (New Zealand)

The geothermal reservoir at Waiwera has been subject to active exploitation for a long time. It is located below the village on the Northern Island of New Zealand and has been used commercially since 1863. The continuous production of geothermal water, to supply hotels and spas, had a negative impact on the reservoir. So far, the physical relation between abstraction rates and water level change of the hydrogeological system is only fairly understood. The aim of this work was to link the influence of rates to the measured data to derive reservoir properties. For this purpose, the daily abstraction history was investigated by means of a variable production rate well test analysis. For the analysis, a modified deconvolution algorithm was implemented. The algorithm derives the reservoir response function by solving a least square problem with the unique feature of imposing only implicit constraints on the solution space. To further investigate the theoretical performance of the algorithm a simulation with synthetic data was conducted for three possible reservoir scenarios. Results throughout all years indicate radial flow during middle-time behaviour and a leaky flow boundary during late-time behaviour. For middle-time behaviour, the findings agree very well with prior results of a pumping test. For the future, a more extensive investigation of different flow conditions under different parametrisations should be conducted.

Analytical solution to pressure transient analysis in non-Newtonian/Newtonian fluids in naturally fractured composite reservoirs

During polymer flooding into the reservoir using shear-thinning non-Newtonian fluids, an interface exists between the in situ Newtonian crude oil in the reservoir and the injected polymer solution. This paper examines the application of non-Newtonian well test analysis techniques to develop an analytical solution to non-Newtonian/Newtonian fluids with different flow index composite reservoirs for estimating the interface boundary conditions in such reservoirs. Mathematical models were presented and solved analytically using the Laplace transform. The solution was inverted to real domain with the Stehfest algorithm (Stehfest Commun ACM, 1970). Solutions were obtained for three boundary conditions that are infinite acting, constant pressure and no-flow boundaries. New pressure and pressure derivative type curves are developed for naturally fractured reservoirs. A general solution was obtained, which is adaptable to the case of non-Newtonian/Newtonian fluid interface. Numerical examples are used to estimate the radius of investigation, reservoir and power-law flow parameters using type curve matching and Tiab’s direct synthesis technique.

Calibration of naturally fractured reservoir models using integrated well-test analysis – an illustration with field data from the Barents Sea

This paper successfully applied the geoengineering workflow for integrated well-test analysis to characterise fluid flow in a newly discovered fractured reservoir in the Barents Sea. A reservoir model containing fractures and matrix was built and calibrated using this workflow to match complex pressure transients measured in the field. We outline different geological scenarios that could potentially reproduce the pressure response observed in the field, highlighting the challenge of non-uniqueness when analysing well-test data. However, integrating other field data into the analysis allowed us to narrow the range of uncertainty, enabling the most plausible geological scenario to be taken forward for more detailed reservoir characterisation and history matching. The results provide new insights into the reservoir geology and the key flow processes that generate the pressure response observed in the field. This paper demonstrates that the geoengineering workflow used here can be applied to better characterise naturally fractured reservoirs. We also provide reference solutions for interpreting well-tests in fractured reservoirs where troughs in the pressure derivative are recognisable in the data.

Automatic reservoir model identification method based on convolutional neural network

Well test analysis is a crucial technique to monitor reservoir performance, which is based on the theory of seepage mechanics, through the study of well test data, to identify reservoir models and estimate reservoir parameters. Reservoir model recognition is the first and essential step of well test analysis. It is usually judged by professionals' experience, which results in low efficiency and accuracy. This paper is devoted to applying convolutional neural network (CNN) to well test analysis and proposes a new intelligent reservoir model identification method. Eight reservoir models studied in this paper include homogenous reservoirs with different outer boundaries such as infinite acting boundary, circular, single, angular, channel, U-shaped and rectangular sealing fault boundaries and a radial composite reservoir with infinite acting boundary. Well testing data used in this paper, including actual field data and theoretical data generated by analytical solutions. To improve the classification accuracy of actual field data, noise processing was carried out on the data before training. The CNN that is most suitable for model recognition has been obtained through trial-and-error procedures. The availability of proposed CNN is proved with actual field cases of Daqing oil field, China. The method realizes the automatic identification of reservoir model with the total classification accuracy (TCA) of test data set of 98.68% and 95.18% for original data and noisy data respectively.

Study on Evaluation Method of Water Injection Efficiency in Low-Permeability Reservoir

Low-permeability reservoirs, especially ultralow-permeability reservoirs, usually show a problem of ineffective water injection which leads to low pressure with high injection-production ratio. It is urgent to determine the direction and proportion of ineffective water injection, so as to guide the adjustment of water injection development. Based on the theory of percolation mechanics and combined with the modern well test analysis method, the determination method of effective water injection ratio was established. This method can not only judge the direction of injected water but also determine the proportion of invalid injected water. This method was applied on typical oil reservoirs; the evaluation results showed that extremely low permeability and ultralow permeability usually exist the situation of water holding around the injected well which is almost 20% of the injected water. Some areas existed the water channeling; the evaluation results showed that the water channeling was closely related with sedimentary microfacies rather than microfractures, and the invalid injection accounts are about 45% of the injected water. The method is simple and feasible, which can provide technical reference for the development strategy adjustment of water drive development in low-permeability reservoir.