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M1 reservoir is a large multi-layered sandstone reservoir in Middle East, which is under primary depletion and edge aquifer drive. There are lots of sources of water production data in M1, and water production data are one of the most important and invaluable surveillance data to understand reservoir connectivity. This paper proposes a method to show how to integrate all sources of aquifer influx surveillance data to evaluate reservoir connectivity of M1. There are four types of aquifer influx identification data in M1 reservoir, and different type of surveillance data are analyzed in detail. Through aquifer influx analysis, it can be confirmed if wells are aquifer flooded in some zones. Then, combined geological understanding with well aquifer breakthrough time and well water cut change characteristic analysis, the possible aquifer influx zone is determined. Finally, aquifer support and sand body connectivity around water flooded wells are better understood, which is helpful and useful for next waterflooding development. M1 reservoir is a large multi-layered sandstone reservoir of deltaic environment with oil bearing area around 500Km2 in Middle East. And M1 is influenced by fluvial, tide and wave, which results in great variations of sand bodies' distribution, reservoir quality and connectivity. Furthermore, M1 reservoir is still under primary depletion with reservoir pressure close to saturation pressure, so waterflooding should be applied urgently. Four types of data were analyzed to study the aquifer influx, which including measured water cut data, flowtest data, saturation logging data and SGS data. Through data analysis, the confirmed aquifer-influx wells and possible aquifer -influx wells are determined, and water breakthrough time and water cut change characteristic are also determined. And based on the characteristic, four areas with different characteristic are classified. Combined with the geological understanding, it is found that the connectivity within each area are similar, but there are barriers among different areas which results in poor communication among different areas. Also the water breakthrough zone of each area are different, and it is useful to understanding aquifer support and reservoir lateral heterogeneity of different zones. Furthermore, aquifer influx has preferred direction, which mainly moves along with the channels axis. This phenomenon should be considered in well pattern decision making during the following waterflooding study. This paper offers a case study of reservoir connectivity analysis based on different types of aquifer influx surveillance data analysis. And the understanding is also much valuable and useful for depositional facies mapping and the next waterflooding well pattern selection and decision. It also provide a reference for the related study on other similar field.
M1 reservoir is a large multi-layered sandstone reservoir in Middle East, which is under primary depletion and edge aquifer drive. There are lots of sources of water production data in M1, and water production data are one of the most important and invaluable surveillance data to understand reservoir connectivity. This paper proposes a method to show how to integrate all sources of aquifer influx surveillance data to evaluate reservoir connectivity of M1. There are four types of aquifer influx identification data in M1 reservoir, and different type of surveillance data are analyzed in detail. Through aquifer influx analysis, it can be confirmed if wells are aquifer flooded in some zones. Then, combined geological understanding with well aquifer breakthrough time and well water cut change characteristic analysis, the possible aquifer influx zone is determined. Finally, aquifer support and sand body connectivity around water flooded wells are better understood, which is helpful and useful for next waterflooding development. M1 reservoir is a large multi-layered sandstone reservoir of deltaic environment with oil bearing area around 500Km2 in Middle East. And M1 is influenced by fluvial, tide and wave, which results in great variations of sand bodies' distribution, reservoir quality and connectivity. Furthermore, M1 reservoir is still under primary depletion with reservoir pressure close to saturation pressure, so waterflooding should be applied urgently. Four types of data were analyzed to study the aquifer influx, which including measured water cut data, flowtest data, saturation logging data and SGS data. Through data analysis, the confirmed aquifer-influx wells and possible aquifer -influx wells are determined, and water breakthrough time and water cut change characteristic are also determined. And based on the characteristic, four areas with different characteristic are classified. Combined with the geological understanding, it is found that the connectivity within each area are similar, but there are barriers among different areas which results in poor communication among different areas. Also the water breakthrough zone of each area are different, and it is useful to understanding aquifer support and reservoir lateral heterogeneity of different zones. Furthermore, aquifer influx has preferred direction, which mainly moves along with the channels axis. This phenomenon should be considered in well pattern decision making during the following waterflooding study. This paper offers a case study of reservoir connectivity analysis based on different types of aquifer influx surveillance data analysis. And the understanding is also much valuable and useful for depositional facies mapping and the next waterflooding well pattern selection and decision. It also provide a reference for the related study on other similar field.
High water-cut has been observed for many multi-fractured horizontal wells (MFHWs) in China soon after waterflooding begins. Available well-testing models of single well ignored the effect of adjacent wells on the MFHW, and they are unable to evaluate whether MFHW (producer) and surrounding vertical wells (injectors) are in good pressure communication. To fill this gap, this work presents a multi-well interference testing (MWIT) model to consider the interference of injectors and further match the interference pressure data. The MWIT model is established to investigate the effect of multiple injection wells on transient-pressure behavior of the MFHW. Due to the interferences from injectors, the pressure and pressure-derivative curves of MWIT move down beginning with the biradial flow regime for single MFHW model, and pseudo-radial flow (horizontal line with the value of 0.5 on pressure-derivative curve) disappears. Sensitivity analysis was conducted to discuss the effects of crucial parameters on the pressure response, including total injection rates, unequal injection rates of injectors, well spacing, injector distribution, number and production of hydraulic fractures. When total injection rates are lower than the production rate, the pressure derivative will eventually stabilize at 0.5*(1-Σ(qIncjD)) during the interference-flow regime on the log-log type curves. Since only the positive number can be shown in the log-log graph, semi-log curves are also developed to fully characterize the flow regimes of MWIT. A novel finding is that pressure derivative also ultimately behave as a horizontal line with the value of 0.5*(1-Σ(qIncjD)) when total injection rates are equal or higher than production rates on the semi-log curves. The total injection rates and well spacing between the MFHW and injectors have a significant effect on middle and late pressure behaviors, whereas the number and production of fractures mainly affects the pressure responses during early to middle period. Type curves indicate that the effect of surrounding injectors are significant and cannot be ignored, and the novel characteristics provide potential application of the MWIT model to estimate formation parameters. Case studies highlight the application of the proposed method in effectively matching the interference pressure data. Interference-testing analysis of the MWIT provides a better reservoir evaluation compared to single-well testing model.
We present a method to obtain a 2D description of a reservoir in terms of transmissivity and diffusivity estimated from well testing. This technique is based on the averaging of the parameters in a proposed interference-test measurement area. This area depends on time, diffusivity, and pressure-gauge sensitivity. We discuss a field application in a heterogeneous, anisotropic reservoir, and we use a simulator that uses oiVwater production data from a pilot water-injection test to validate the application's results. 2132200 2130100 • 4 ( I J \ / I / • 12 r...--, / \ / / J J / / /
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