Forecasting of Steamflood Performance in a Heavy Oil Field

Abstract

Low oil recovery reached after more than 60 years of production in a rather deep heavy oil (9.5-11°API) field in South America encourages looking for a thermal method (steamflood) which potentially promises a tenfold increase in the recovery factor.Steamflood forecasting for such a large field is highly challenging. Direct thermal reservoir simulation seems impractical because of the size of the model (about 120 million grid-blocks). A recently developed correlation-based probabilistic technique has met the forecasting challenge. First, a relatively small set of thermal reservoir simulation runs with sector models provided a basis for production/injection correlations between key rates as well as cumulative quantities and the models' geologic and engineering input. All the runs included pressure depletion and steamflood stages. Next, about 350 forecasting elements (1 sq. km each) covered the entire Northern part of the field, which was subject of the forecasting; every element received probabilistic distributions of the key geologic parameters involved in correlations. Then, the Monte-Carlo simulation united the correlations with the distributions and allowed selecting combinations of the parameters corresponding to P10-P50-P90 probabilities of the dynamic performance. Reconstruction of oil and water production as well as steam injection P10-P50-P90 time-profiles completed preparation of the forecasting elements.Reservoir depth and quality cut-offs restricted the area of the potential future steamflood application. For several sets of the cut-offs, wells were placed within the areas in accordance with inverted 7-spot patterns at either 8 or 24 acres/pattern. Every well adopted production or injection profiles from its forecasting element-host. Drilling timetable controlled the wells coming on primary production. Steamflood started in a 100-pattern cluster after 4-5 years of primary production. Heat management governed time-profile of steam injection into the cluster. On reaching a steam-oil-ratio cut-off, steam injection shifted to the next 100-pattern cluster. Summarizing individual well production or injection time-profiles, arranged in accordance with the drilling schedule, generated field P10-P50-P90 forecasts for several cases of interest. 157

Steamflood Injection Performance on 2 Types of Sand Heterogeneity in XYZ Field

“…Similar findings were found by Rubin and Izgec [21] about the distribution of heat, and an increase in temperature was identified as a factor in the expansion of oil output. Compared to the clean sand in the X heavy oil field, the fining sand underperformed for steamflood injection, although cumulative oil output was still somewhat substantial.…”

“…This phenomenon was caused by the decreased pressure drawdown seen by the well spacing of 23-acre as shown in Figure 5(c) is slightly lower than the 32,374.90 m 2 well spacing in base case scenarios. Similar results were obtained by Rubin and Izgec [21], indicating that a larger well spacing would reduce well interferences and improve conditions for primary production recovery.…”

“…Nonetheless, the larger well spacing and lower steam quality diminished the heat transfer effectiveness because the heated zone was still distant from the production well, causing the oil viscosity around the producer to remain elevated and its highest temperature to reach 276.66 ○ C in the injection well area. These findings were supported by Rubin and Izgec [21], who asserted that the larger well spacing decreased oil production due to a delay in communication between injectors and producers and a decrease in steam injection unit of area.…”

“…In the X field, Rubin and Izgec [21] analyzed the fining sand formation with a permeability distribution between 6.90e-13 m 2 and 1.28e-13 m 2 . The results demonstrated that steamflood injection enhanced oil production, with the 32,375 m 2 well spacing producing 16.5 MMm 3 of oil compared to the 97,125 m 2 well spacing's 8.8 MMm3.…”