Predict Reservoir Fluid Properties from Advanced Mud Gas Data

Abstract

In a recent paper, we published a machine learning method to quantitatively predict reservoir fluid gas oil ratio (GOR) from advanced mud gas (AMG) data. The significant increase of the model accuracy compared to traditional modeling approaches makes it possible to estimate reservoir fluid GOR based on AMG data while drilling, before the wireline operation. This approach has clear advantages due to early access, low cost, and a continuous reservoir fluid GOR for all reservoir zones. In this paper, we release further study results to predict other fluid properties besides GOR, which is essential for geo-operations, field development plans, and production optimization. We use two approaches to predict other fluid properties. For the first approach, we develop machine learning models for reservoir fluid density, similar to the GOR prediction approach in the previous paper. Based on an extensive fluid database, we establish machine learning models to predict reservoir fluid density from C1 to C5 compositions (same format as AMG data). As the second approach, instead of developing a machine learning model for individual fluid property from C1 to C5 compositions, we investigate the most important properties for EOS fluid characterization: C6 and C7+ compositions, and molecular weight and density of C7+ fraction. Once these properties are in place, the entire spectrum of fluid properties can be calculated with the equation of state (EOS) model. The results of fluid property prediction are satisfactory with both approaches. The reservoir oil density prediction has a mean average error (MAE) of 0.039 g/cm3. The accuracy is superior to typical density derived from the pressure gradient from wireline logging data. For the essential fluid properties required for EOS model prediction, the overall accuracy is acceptable compared with laboratory measurements. The reservoir fluid properties predicted from the EOS model are similar or better than the predictions from individual machine learning models. We applied the field measured AMG data into the fluid property models and achieved reasonable results, as illustrated by the reservoir fluid density example. The paper completed the methodology to predict all fluid properties based on AMG data. This work paves the way to generate a complete reservoir fluid log for all relevant fluid properties. The method has a significant business impact due to the full coverage of reservoir fluid properties along the well path compared with the availability of discrete PVT samples. The advantage of providing fluid properties in all reservoir zones along the well path far outweighs the limitation of somewhat reduced fluid property accuracy.