Dana Petroleum Optimizes Well Placement Decisions in Low-Data Environment, Nefertiti Field | Schlumberger
Case Study
Egypt, Africa, Onshore

Challenge:  Accurately characterize the reservoir despite low data availability to evaluate potential field development plan.

Solution: Use a neural network in the Petrel E&P software platform to efficiently determine the permeability distribution and then conduct a history match for the already depleted field prior to modeling different development scenarios in the ECLIPSE industry-reference reservoir simulator.

Results: Confidently assessed field development plan after obtaining a strong history match for the model by creating an aquifer in the Petrel platform to account for the observed pressure depletion.

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Dana Petroleum Optimizes Well Placement Decisions in Low-Data Environment, Nefertiti Field

Neural network in the Petrel platform and modeling in the ECLIPSE reservoir simulator create highly representative geological models

Little data yields low-confidence model

Dana Petroleum's challenge in evaluating Nefertiti Field, which is adjacent to a mature development field, was that very little information had been collected to characterize reservoir conditions within a static model: only conventional core analysis and a nuclear magnetic resonance (NMR) log from a single well.

Low-data fields severely limit confidence in the reservoir model, which in turn increases the risk posed by costly field decisions. Many assumptions and correlations must be made to augment the data in constructing a representative reservoir model, and each of these adds to the uncertainty.

Neural network efficiently determines permeability distribution

Schlumberger proposed using the Petrel E&P software platform to establish relationships between the core analysis and log interpretations for delineating spatial trends across the reservoir. In the absence of a facies model, a neural network was created in the Petrel platform. The routine logs, such as gamma ray, obtained with the NMR log were used to precondition the algorithm for predicting the flow zone index (FZI) for the wells across the permeability distribution. Not only did using a neural network save a significant amount of time, but it fully represented reservoir heterogeneity even though the porosity distribution and NMR data were available from only one well.

History-matched dynamic model thoroughly assesses development options

Following determination of the permeability distribution for the whole model and creation of relative permeability and capillary pressure curves, the dynamic model was ready for initialization. Dynamic modeling honored all the static model and data preparation uncertainties in estimating the original oil in place (OOIP) and the P10, P50, and P90 values to support asset valuation and field development planning.

However, before the development plan could be initiated, it was critical to achieve a history match. The pressure match was the most challenging because Nefertiti Field was initially depleted. This was remedied by creating an aquifer in the Petrel platform model to accurately reflect the pressure response of the reservoir. With a successful history match, the model could then be used to evaluate production scenarios.

The different development plans assessed with the matched model were all focused on optimal well placement of the producers and injectors. Using the Petrel platform and ECLIPSE simulator made it easy to compare different trajectories and well controls, and two new producer locations were agreed on for drilling. To maintain reservoir pressure throughout development, a waterflooding scenario was assessed within the Petrel platform and ECLIPSE simulator, and three new injectors were confidently recommended for inclusion in the next drilling schedule.

Dana Petroleum Optimizes Well Placement Decisions in Low-Data Environment, Nefertiti Field
The permeability predicted by the neural network was well matched to the NMR permeability.
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Despite the low-data environment and lack of a facies model, the neural network in the Petrel platform accounted for reservoir heterogeneity in efficiently generating the permeability distribution.
“Neural network is a powerful tool to generate permeability based on the flow zone index (FZI). Adding an aquifer to the model compensated the effect of nearby producing field that resulted in history matching and the field development plan.”

Ahmed Al-Kalamawi, Subsurface Team Manager Dana Petroleum

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