Page 288 - Introduction to Petroleum Engineering

276 RESERVOIR PERFORMANCE
TABLE 14.1 Brown Field Flow Modeling Workflow
Step Task

B1 Gather data
B2 Identify key parameters and associated uncertainties
B3 Identify history match criteria and history match variables
B4 Generate forecast of field performance results
B5 Determine quality of history match
B6 Generate distribution of field performance results
B7 Verify workflow
Source: Fanchi (2010).
distributions can be used to characterize parameter uncertainty. A set of production fore‑
casts is generated by sampling the probability distributions and developing a realization
of the reservoir for each set of sampled parameter values. Reservoir performance is cal‑
culated for each realization and a distribution of recovery results is prepared. In the case
of green fields, the results are relatively unconstrained by historical production.
The workflow for brown fields differs from the green field workflow because his‑
torical data is available to constrain the set of results used to generate a distribution of
recovery forecasts. Two brown field workflows are currently being used in industry:
deterministic reservoir forecasting and probabilistic reservoir forecasting. In deter‑
ministic reservoir forecasting, a single reservoir realization is selected and matched to
historical performance. The history match is used to calibrate the flow model before
the forecast is made. In probabilistic reservoir forecasting, a statistically significant
collection, or ensemble, of reservoir realizations is prepared. Dynamic models are run
for each possible realization, and the results of the dynamic model runs are then com‑
pared to historical performance of the reservoir. The workflow in Table 14.1 presents
the steps for conducting a probabilistic brown field flow modeling workflow.

Example 14.3 Brown Field Model Reserves
Estimate P , P , P reserves for a brown field with a normal distribution of
90
10
50
reserves. The distribution has a mean of 255 MSTBO and a standard deviation
of 25 MSTBO.
Answer
Provedreserves = P 90 = µ −128σ = 223 MSTBO
.
Probablereserves = P 50 = µ = 255 MSTBO
Possiblereserves = P 10 = µ +128σ = 287 MSTBO
.

14.3 PERFORMANCE OF CONVENTIONAL OIL AND gAS RESERVOIRS

We have introduced many factors that affect the performance of reservoirs in previous
chapters. For example, primary depletion of oil reservoirs depends on the natural
drive mechanisms discussed in Chapter 13. In this section we consider examples of
reservoir performance of conventional oil and gas reservoirs.