Determining Well-to-Well Connectivity in Tight Reservoirs

Mirzayev, Mammad; Riazi, Naimeh; Cronkwright, David; Jensen, Jerry L.; Pedersen, Per Kent

doi:10.2118/175943-ms

Cited by 6 publications

(1 citation statement)

References 16 publications

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“…Therefore, it is important to emphasize that the CRM-derived interwell connectivities are mainly related to the pressure support while streamlines allocation factors are related to the fraction of injected fluid flowing towards a producer. As recently exemplified and discussed by Mirzayev et al [48] for waterflooding in tight reservoirs and by Holanda et al [33] for conventional reservoirs, these differences can be further explored when studying the distribution of flow paths and barriers in the reservoir.…”

Section: Comparisons Of Crm Interwell Connectivities With Streamline mentioning

confidence: 97%

A State-of-the-Art Literature Review on Capacitance Resistance Models for Reservoir Characterization and Performance Forecasting

et al. 2018

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Capacitance resistance models (CRMs) comprise a family of material balance reservoir models that have been applied to primary, secondary and tertiary recovery processes. CRMs predict well flow rates based solely on previously observed production and injection rates, and producers’ bottomhole pressures (BHPs); i.e., a geological model and rock/fluid properties are not required. CRMs can accelerate the learning curve of the geological analysis by providing interwell connectivity maps to corroborate features such as sealing faults and channels, as well as diagnostic plots to determine sweep efficiency and reservoir compartmentalization. Additionally, it is possible to compute oil and water rates by coupling a fractional flow model to CRMs which enables, for example, optimization of injected fluids allocation in mature fields. This literature review covers the spectrum of the CRM theory and conventional reservoir field applications, critically discussing their advantages and limitations, and recommending potential improvements. This review is timely because over the last decade there has been a significant increase in the number of publications in this subject; however, a paper dedicated to summarize them has not yet been presented.

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Section: Comparisons Of Crm Interwell Connectivities With Streamline mentioning

confidence: 97%

A State-of-the-Art Literature Review on Capacitance Resistance Models for Reservoir Characterization and Performance Forecasting

et al. 2018

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State-of-the-Art Solution of Capacitance Resistance Model by Considering Dynamic Time Constants as a Realistic Assumption

Lesan

Eshraghi

Bahroudi

et al. 2017

Journal of Energy Resources Technology

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To have an acceptable accuracy for water flooding projects, proper history matching is an important tool. Capacitance resistance model (CRM) simulates water flooding performance based on two tuning parameters of time constant and connectivity. Main advantages of CRM are its simplicity and fastness; furthermore, it needs only some field-available inputs like injection and production flow rates. CRM is reliable if producers receive the injection rate signal; in other words, duration of history matching must be enough so that the rate signal of injection is sensed in producers. It is a shortcoming of CRM that the results might not be accurate as a result of short history. In the common CRM, time constant is considered to be a static parameter (constant number) during the history of simulation. However, time constant is a time-dependent function that depends on the reservoir nature. In this paper, a new model has been developed as it decreases model dependency on the history matching length by shifting time axis. This new definition adds a rate shift constant to the model mathematics. Moreover, a new model is considering dynamic time constants. This new model is called dynamic capacitance resistance model (DCRM). Two reservoir models have been simulated to analyze the performance of DCRM, and, as a result, it is found that the static time constant is an erroneous assumption. Finally, the accuracy of the results has been improved since the degree-of-freedom of the CRM increased in the new version.

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Inferring Well Connectivity in Waterfloods Using Novel Signal Processing Techniques

Wang

Kabir

Reza

2018

SPE Annual Technical Conference and Exhibition

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Data mining has become increasingly crucial in deciphering reservoir dynamics given that operators currently acquire an enormous amount of data. These data contain valuable information about subsurface processes. Interwell connectivity is one of the most significant aspects of subsurface characterization that can impact a project's success. In this study, we present novel techniques to quantify and monitor interwell communication by applying signal processing methods to observed and derived well-based measurements. We construct a suite of realistic reservoir models under varying conditions involving multiple producing and water injection wells. More than 40 static and dynamic parameters including permeability, porosity, water saturation, fluid properties and rock-fluid interaction terms are varied using experimental designs to capture realistic uncertainty. Waterflood scenarios are modeled using streamline simulation to infer injector-producer pair connectivity, pattern allocations, drainage efficiency, and their evolution period. These injector-producer variables are analyzed using numerous signal processing methods including cross-correlation, time-lag correlation coefficient, coherence, and periodogram, among others. Well variables examined involve pressure, rate, and their derivative functions. We employed a thorough and systematic multipronged approach to decipher signal processing of the injector-producer well variables. At one level, the objective was to identify which variables pair provides meaningful connectivity information. Combination of well variables examined includes injector bottomhole pressure (BHP) - producer BHP; injection rates - production rates; injector productivity index (PI) - producer PI; injector well variables - producer gas-oil ratio; and F1 - F2. Here, F1 is a producer well-variable defined as the production rate added to the product of PI and BHP of the producer, while F2 is simply the difference between injection and production rates. The other objective was to identify which signal processing technique(s) will be most useful to infer interwell connectivity. To verify well connectivity, we used streamline simulation allocation pair-wise values. Our analyses indicate cross-correlation of F1 - F2 contain most meaningful connectivity information. Currently, we are investigating more advanced signal-processing techniques to explore if we can retrieve enhanced connectivity information. In this context, we have also compared the performance of our approach with the capacitance-resistance model for validation. A large dataset from the Permian Basin constituted the primary content of this investigation. We examined and devised several signal-processing techniques of well variables to assess interwell connectivity in realistic reservoir settings. The approaches reveal new insights into determining reservoir communication metrics. The proposed methods can be easily implemented in the automated virtual flowmetering system. These methods also help identify the predominant injector-producer pairs and making operational decisions at any stage of any waterflood and enhanced recovery projects.

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Determining Well-to-Well Connectivity in Tight Reservoirs

Cited by 6 publications

References 16 publications

A State-of-the-Art Literature Review on Capacitance Resistance Models for Reservoir Characterization and Performance Forecasting

A State-of-the-Art Literature Review on Capacitance Resistance Models for Reservoir Characterization and Performance Forecasting

State-of-the-Art Solution of Capacitance Resistance Model by Considering Dynamic Time Constants as a Realistic Assumption

Inferring Well Connectivity in Waterfloods Using Novel Signal Processing Techniques

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