Inferring Interwell Connectivity from Well Bottomhole-Pressure Fluctuations in Waterfloods

2008 ◽  
Vol 11 (05) ◽  
pp. 874-881 ◽  
Author(s):  
Djebbar Tiab ◽  
Anh V. Dinh

Summary This paper presents a new procedure to determine interwell connectivity in a reservoir on the basis of fluctuations of bottomhole pressure of both injectors and producers in a waterflood. The method uses a constrained multivariate linear-regression (MLR) analysis to obtain information about permeability trends, channels, and barriers. Previous authors applied the same analysis to injection and production rates to infer connectivity between wells. In order to obtain good results, however, they applied various diffusivity filters to the flow-rate data to account for the time lags and the attenuation. This was a tedious process that requires subjective judgment. Shut-in periods in the data, usually unavoidable when a large number of data points were used, created significant errors in the results and were often eliminated from the analysis. This new method yielded better results compared with the results obtained when production data were used. Its advantages include:no diffusivity filters needed for the analysis,minimal number of data points required to obtain good results,and flexible plan to collect data because all constraints can be controlled at the surface. The new procedure was tested by use of a numerical reservoir simulator. Thus, different cases were run on two fields, one with five injectors and four producers and the other with 25 injectors and 16 producers. For a large waterflood system, multiple wells are present and most of them are active at the same time. In this case, pulse tests or interference tests between two wells are difficult to conduct because the signal can be distorted by other active wells in the reservoir. In the proposed method, interwell connectivity can be obtained quantitatively from multiwell pressure fluctuations without running interference tests. Introduction Well testing is a common and important tool of reservoir characterization. Many well-testing methods have been developed in order to obtain various reservoir properties. Interference tests and pulse tests are used to quantify communication between wells. These methods are often applied to two wells such that one well sending the signals (by changing flow rates) and the other is receiving them (Lee et al. 2003). For a large field such as a waterflood system, however, multiple wells are present, and most of them are active at the same time. In that case, pulse tests or interference tests between two wells are difficult to conduct because the signal can be distorted by other active wells in the reservoir. In this method, data can be obtained from multiwell pressure tests that resemble interference tests. Thus, we can have several wells sending signals and the others receiving the signals at the same time. The wells that are receiving the signal, however, can either be shut in or kept at constant producing rates. The pressures at all wells are recorded simultaneously within a constant time interval. The length of the test will depend on the length of the time interval and the number of data points. Results of this method can be used to optimize operations and economics and enhance oil recovery of existing waterfloods by changing well patterns, changing injection rates, recompletion of wells, and infill drilling. This work is based on previous work conducted by Albertoni and Lake (2003) by use of injection and production rates. In their work, Albertoni and Lake developed and tested different approaches by use of constrained MLR analysis with a numerical simulator and then applied it to a waterflooded field in Argentina. They used diffusivity filters to account for the time lag and attenuation of the data. In his thesis, Dinh (2003) verified the method by use of a different reservoir simulator and applied it to a waterflooded field in Nowata, Oklahoma. He also investigated the effect of shut-in periods and vertical distances on the results. The main objectives of this work are to verify the results obtained from pressure data with results from flow-rate data to propose a new method to determine interwell connectivity and to suggest further research and study on the method. Similar to the method that uses production rates, we will concentrate on a waterflood system only. The reservoir is considered as a system that processes a stimulus (i.e., a well that is sending signals) and returns a response (i.e., a well that is receiving the signals). The effect of the reservoir on the input signal will depend on the location and the orientation of each stimulus/response pair. Because the total pressure changes at active and observation wells are not equal, only the MLR (Albertoni and Lake 2003; Dinh 2003; Albertoni 2002) was used. The effect of diffusion was not significant, thus the diffusivity filters were not used. The method was applied to two synthetic fields, one with five injectors and four producers and the other with 25 injectors and 16 producers.

2020 ◽  
Vol 10 ◽  
pp. 20-40
Author(s):  
Dinh Viet Anh ◽  
Djebbar Tiab

A technique using interwell connectivity is proposed to characterise complex reservoir systems and provide highly detailed information about permeability trends, channels, and barriers in a reservoir. The technique, which uses constrained multivariate linear regression analysis and pseudosteady state solutions of pressure distribution in a closed system, requires a system of signal (or active) wells and response (or observation) wells. Signal wells and response wells can be either producers or injectors. The response well can also be either flowing or shut in. In this study, for consistency, waterflood systems are used where the signal wells are injectors, and the response wells are producers. Different borehole conditions, such as hydraulically fractured vertical wells, horizontal wells, and mixed borehole conditions, are considered in this paper. Multivariate linear regression analysis was used to determine interwell connectivity coefficients from bottomhole pressure data. Pseudosteady state solutions for a vertical well, a well with fully penetrating vertical fractures, and a horizontal well in a closed rectangular reservoir were used to calculate the relative interwell permeability. The results were then used to obtain information on reservoir anisotropy, high-permeability channels, and transmissibility barriers. The cases of hydraulically fractured wells with different fracture half-lengths, horizontal wells with different lateral section lengths, and different lateral directions are also considered. Different synthetic reservoir simulation models are analysed, including homogeneous reservoirs, anisotropic reservoirs, high-permeability-channel reservoirs, partially sealing barriers, and sealing barriers.The main conclusions drawn from this study include: (a) The interwell connectivity determination technique using bottomhole pressure fluctuations can be applied to waterflooded reservoirs that are being depleted by a combination of wells (e.g. hydraulically fractured vertical wells and horizontal wells); (b) Wellbore conditions at the observations wells do not affect interwell connectivity results; and (c) The complex pressure distribution caused by a horizontal well or a hydraulically fractured vertical well can be diagnosed using the pseudosteady state solution and, thus, its connectivity with other wells can be interpreted.


2021 ◽  
Vol 12 (2) ◽  
pp. 480-490
Author(s):  
Ahsanul Salehin ◽  
Ramesh Raj Puri ◽  
Md Hafizur Rahman Hafiz ◽  
Kazuhito Itoh

Colonization of a biofertilizer Bacillus sp. OYK strain, which was isolated from a soil, was compared with three rhizospheric and endophytic Bacillus sp. strains to evaluate the colonization potential of the Bacillus sp. strains with a different origin. Surface-sterilized seeds of tomato (Solanum lycopersicum L. cv. Chika) were sown in the sterilized vermiculite, and four Bacillus sp. strains were each inoculated onto the seed zone. After cultivation in a phytotron, plant growth parameters and populations of the inoculants in the root, shoot, and rhizosphere were determined. In addition, effects of co-inoculation and time interval inoculation of Bacillus sp. F-33 with the other endophytes were examined. All Bacillus sp. strains promoted plant growth except for Bacillus sp. RF-37, and populations of the rhizospheric and endophytic Bacillus sp. strains were 1.4–2.8 orders higher in the tomato plant than that of Bacillus sp. OYK. The plant growth promotion by Bacillus sp. F-33 was reduced by co-inoculation with the other endophytic strains: Klebsiella sp. Sal 1, Enterobacter sp. Sal 3, and Herbaspirillum sp. Sal 6., though the population of Bacillus sp. F-33 maintained or slightly decreased. When Klebsiella sp. Sal 1 was inoculated after Bacillus sp. F-33, the plant growth-promoting effects by Bacillus sp. F-33 were reduced without a reduction of its population, while when Bacillus sp. F-33 was inoculated after Klebsiella sp. Sal 1, the effects were increased in spite of the reduction of its population. Klebsiella sp. Sal 1 colonized dominantly under both conditions. The higher population of rhizospheric and endophytic Bacillus sp. in the plant suggests the importance of the origin of the strains for their colonization. The plant growth promotion and colonization potentials were independently affected by the co-existing microorganisms.


1957 ◽  
Vol 35 (3) ◽  
pp. 324-331 ◽  
Author(s):  
W. A. Prowse ◽  
G. R. Bainbridge

A high voltage pulse lasting 0.35 microsecond is applied to a pair of delay lines, so that two pulses can be picked up from adjustable points of connection on the lines. One is applied to an irradiating gap and the other to a longer test gap, the gaps being so arranged that only mid-gap irradiation occurs. The sparking probability, P, of the test gap is used to indicate the presence of ionizing radiation. Variations of P with the time interval between the two pulses are recorded. They indicate that ionizing radiation is emitted in repeated short flashes. Photographic observations support this view.


2021 ◽  
Author(s):  
Yuzhe Cai ◽  
Arash Dahi Taleghani

Abstract Infill completions have been explored by many operators in the last few years as a strategy to increase ultimate recovery from unconventional shale oil reservoirs. The stimulation of infill wells often causes pressure increases, known as fracture-driven interactions (FDIs), in nearby wells. Studies have generally focused on the propagation of fractures from infill wells and pressure changes in treatment wells rather than observation wells. Meanwhile, studies regarding the pressure response in the observation (parent) wells are mainly limited to field observations and conjecture. In this study, we provide a partialcorrective to this gap in the research.We model the pressure fluctuations in parent wells induced by fracking infill wells and provide insight into how field operators can use the pressure data from nearby wells to identify different forms of FDI, including fracture hit (frac-hit) and fracture shadowing. First,we model the trajectory of a fracture propagating from an infill well using the extended finite element methods (XFEM). This method allows us to incorporatethe possible intersection of fractures independent of the mesh gridding. Subsequently, we calculate the pressure response from the frac-hit and stress shadowing using a coupled geomechanics and multi-phase fluid flow model. Through numerical examples, we assess different scenarios that might arise because of the interactions between new fractures and old depleted fractures based on the corresponding pressure behavior in the parent wells. Typically, a large increase in bottomhole pressure over a short period is interpreted as a potential indication of a fracture hit. However, we show that a slower increase in bottomhole pressure may also imply a fracture hit, especially if gas repressurization was performed before the infill well was fracked. Ultimately, we find that well storage may buffer the sudden increase in pressure due to the frac-hit. We conclude by summarizing the different FDIs through their pressure footprints.


2021 ◽  
Author(s):  
Soumi Chaki ◽  
Yevgeniy Zagayevskiy ◽  
Terry Wong

Abstract This paper proposes a deep learning-based framework for proxy flow modeling to predict gridded dynamic petroleum reservoir properties (like pressure and saturation) and production rates for wells in a single framework. It approximates the solution of a full physics-based numerical reservoir simulator, but runs much more rapidly, allowing users to generate results for a much wider range of scenarios in a given time than could be done with a full physics simulator. The proxy can be used for reservoir management tasks like history matching, uncertainty quantification, and field development optimization. A deep-learning based methodology for accurate proxy-flow modeling is presented which combines U-Net (a variant of convolutional neural network) to predict gridded dynamic properties and deep neural network (DNN) models to forecast well production rates. First, gridded dynamic properties, such as reservoir pressure and phase saturations, are predicted from static properties like reservoir rock porosity and absolute permeability using a U-Net. Then, the static properties and the dynamic properties predicted by the U-Net are input to a DNN to predict production rates at the well perforations. The inclusion of U-net predicted pressure and saturations improves the quality of the well rate predictions. The proposed methodology is presented with the synthetic Brugge reservoir discretized into grid blocks. The U-Net input consists of three properties: dynamic gridded reservoir properties (such as pressure or fluid saturation) at the current state, static gridded porosity, and static gridded permeability. The U-Net has only one output property, the target gridded property (such as pressure or saturation) at the next time step. Training and testing datasets are generated by running 13 full physics flow simulations and dividing them in a 12:1 ratio. Nine U-Net models are calibrated to predict pressures/saturations, one for each of the nine grid layers present in the Brugge model. These outputs are then concatenated to obtain the complete pressure/saturation model for all nine layers. The constructed U-Net models match the distributions of generated pressures/saturations of the numerical reservoir simulator with a correlation coefficient value of approximately 0.99 and above 95% accuracy. The DNN models approximate well production rates accurately from U-Net predicted pressures and saturations along with static properties like transmissibility and horizontal permeability. For each well and each well perforation, the production rate is predicted with the DNN model. The use of the constructed proxy flow model generates reservoir predictions within a few minutes compared to the hours or days typically taken by a full physics flow simulator. The direct connection that is established between the gridded static and dynamic properties of the reservoir and well production rates using U-Net and DNN models has not been presented previously. Using only a small number of runs for its training, the workflow matches the numerical reservoir simulator results with reduced computational effort. This helps reservoir engineers make informed decisions more quickly, resulting in more efficient reservoir management.


Author(s):  
Sachin Dahikar ◽  
Ram Sonolikar

Local instantaneous pressure signals obtained through a magneto-fluidized bed have been analyzed using both classical and advanced signal analysis methods, which can deliver the necessary information about the presence of the bubbling and turbulent flow pattern. The conventional signal processing tool such as autocorrelation and cross correlation were used as preliminary tools to analyze the data. Evaluation of the dominant bubble frequency was completed using the autocorrelation function and power spectral density function. Mutual information function was used to identify the periodicity and the predictability of the local instantaneous pressure signals. Since it does not demand any particular functional relationships between the data points, it is a better method (compared to autocorrelation function) for measuring the predictability of nonlinear systems.


2021 ◽  
Author(s):  
Ildar Radikovich Abdrakhmanov ◽  
Evgenii Alekseevich Kanin ◽  
Sergei Andreevich Boronin ◽  
Evgeny Vladimirovich Burnaev ◽  
Andrei Aleksandrovich Osiptsov

Abstract We propose a novel approach to data-driven modeling of a transient production of oil wells. We apply the transformer-based neural networks trained on the multivariate time series composed of various parameters of oil wells measured during their exploitation. By tuning the machine learning models for a single well (ignoring the effect of neighboring wells) on the open-source field datasets, we demonstrate that transformer outperforms recurrent neural networks with LSTM/GRU cells in the forecasting of the bottomhole pressure dynamics. We apply the transfer learning procedure to the transformer-based surrogate model, which includes the initial training on the dataset from a certain well and additional tuning of the model's weights on the dataset from a target well. Transfer learning approach helps to improve the prediction capability of the model. Next, we generalize the single-well model based on the transformer architecture for multiple wells to simulate complex transient oilfield-level patterns. In other words, we create the global model which deals with the dataset, comprised of the production history from multiple wells, and allows for capturing the well interference resulting in more accurate prediction of the bottomhole pressure or flow rate evolutions for each well under consideration. The developed instruments for a single-well and oilfield-scale modelling can be used to optimize the production process by selecting the operating regime and submersible equipment to increase the hydrocarbon recovery. In addition, the models can be helpful to perform well-testing avoiding costly shut-in operations.


2021 ◽  
Author(s):  
Artur Aslanyan ◽  
Bulat Ganiev ◽  
Azat Lutfullin ◽  
Ildar Z. Farhutdinov ◽  
Danila Gulyaev ◽  
...  

Abstract Brown fields that are currently experiencing production decline can benefit a lot from production enhancement operations based on localization of residual reserves and geology clarification. The set of solutions includes targeted recommendations for additional well surveys followed by producers and injectors workovers, like whole wellbore or selective stimulation, polymer flow conformance, hydraulic fracturing and side tracking. As a result, previously poorly drained areas are involved in production, which increases current rates and ultimate recovery. The integrated technology of residual reserves localization and production increase includes: Primary analysis of the production history for reservoir blocks ranking by production increase potential. Advanced bottom-hole pressures and production history analysis by multiwell deconvolution for pressure maintenance system optimization and production enhancement. Advanced production logging for flow profile and production layer-by-layer allocation. Conducting pulse-code interference testing for average saturation between wells estimation. 3D reservoir dynamic model calibration on advanced tests findings. Multi-scenario development planning for the scenario with biggest NPV regarding surface infrastructure. The presented integrated technology is carried stage by stage. Based on the data analysis at the first stage (the Prime analysis) it is possible to get three types of results. The top-level assessment of the current development opportunities of the area, evaluation of current residual reserves on base of displacement sweep efficiency estimation, and evaluation of the potential production increase for various blocks of the field. Results of the second stage were obtained for the block deemed with the highest potential for production increase. Those results may reveal possible complications, and relevant workovers can be advised along with additional surveys that can further help to locate current reserves. The last stage of Prime analysis provides the most suitable choice was to perform an advanced logging and well-testing, as they include both single-well and multi-well tests. Pulse-code interference tests, multi-well retrospective tests and reservoir-oriented production logging make it possible to scan the reservoir laterally and vertically, which is especially important for multi-layered fields. The reservoir parameters obtained from the test results are used to calibrate the dynamic reservoir model. The effects of production enhancement operations are calculated from the 3D model. The set of possible activities is evaluated in terms of their financial efficiency based on the economic model of the operator company using multi-scenario approach on a specifically created digital twin of the field. The unique feature of this approach lies in an integrated usage of advanced production history analysis, advanced logging and well-testing technologies, as well as further calibration of the dynamic reservoir model based on test results and used-friendly interface for field digital twin interaction. This paper demonstrates on how to use the field tests results to calibrate the reservoir model and increase the accuracy of production forecasting by reducing the model uncertainty, with intent to increase profit of brownfields.


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