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In a recent paper (Yang et al., 2019a), we published a machine learning method to quantitatively predict reservoir fluid properties from advanced mud gas (AMG) data. This approach has clear advantages due to early access, low cost, and a continuous reservoir fluid prediction for all reservoir zones. In this paper, we demonstrate how real time reservoir fluid logs are generated and compare the results with PVT samples or production data from the same well. We develop a workflow of generating reservoir fluid logs from AMG data and PVT database. The workflow consists of two main processes; first a quality assessment of AMG data and second the computation of reservoir fluid properties (in this paper we use gas oil ratio). The entire workflow is written in python and embedded into existing commercial petrophysics softwares. The final product of the workflow are three log tracks which we call the reservoir fluid logs and those are 1) the concentration readings of the AMG data, 2) the QC metric score, and 3) the predicted GOR log. These three logs are plotted together with other standard open hole logs such as gamma ray, neutron-density, sonic and resistivity log to get a more comprehensive formation evaluation. Reservoir fluid logs derived from AMG data has two main advantages. First, it is the only approach to acquire continuous reservoir fluid properties along the well path. The continuous fluid profile can be used to understand the variation of reservoir fluids in both vertical and lateral direction. The second advantage is that the reservoir fluid log is obtained while drilling and therefore the information can be used to optimize the drilling process or the downhole sampling program during wireline operation. In this paper, we demonstrate the application of the reservoir fluid logs in four conventional field cases. In the first study case we show the benefit of using the reservoir fluid logs in a horizontal well as a substitute for downhole fluid sampling. In the second case study, we demonstrate how the reservoir fluid log is utilized to optimize the downhole fluid sampling program which results in reducing the subsurface uncertainty. Next, we exhibit the use of the reservoir fluid logs to locate gas oil contact in a case where pressure data does not show clear distinction of gas and oil gradient in the reservoir. In the last example, we illustrate the use of reservoir fluid knowledge from AMG to characterizing the fluid variation across a field. The field applications demonstrate the success of the new method for conventional reservoirs, provided good-quality AMG data are available.
In a recent paper (Yang et al., 2019a), we published a machine learning method to quantitatively predict reservoir fluid properties from advanced mud gas (AMG) data. This approach has clear advantages due to early access, low cost, and a continuous reservoir fluid prediction for all reservoir zones. In this paper, we demonstrate how real time reservoir fluid logs are generated and compare the results with PVT samples or production data from the same well. We develop a workflow of generating reservoir fluid logs from AMG data and PVT database. The workflow consists of two main processes; first a quality assessment of AMG data and second the computation of reservoir fluid properties (in this paper we use gas oil ratio). The entire workflow is written in python and embedded into existing commercial petrophysics softwares. The final product of the workflow are three log tracks which we call the reservoir fluid logs and those are 1) the concentration readings of the AMG data, 2) the QC metric score, and 3) the predicted GOR log. These three logs are plotted together with other standard open hole logs such as gamma ray, neutron-density, sonic and resistivity log to get a more comprehensive formation evaluation. Reservoir fluid logs derived from AMG data has two main advantages. First, it is the only approach to acquire continuous reservoir fluid properties along the well path. The continuous fluid profile can be used to understand the variation of reservoir fluids in both vertical and lateral direction. The second advantage is that the reservoir fluid log is obtained while drilling and therefore the information can be used to optimize the drilling process or the downhole sampling program during wireline operation. In this paper, we demonstrate the application of the reservoir fluid logs in four conventional field cases. In the first study case we show the benefit of using the reservoir fluid logs in a horizontal well as a substitute for downhole fluid sampling. In the second case study, we demonstrate how the reservoir fluid log is utilized to optimize the downhole fluid sampling program which results in reducing the subsurface uncertainty. Next, we exhibit the use of the reservoir fluid logs to locate gas oil contact in a case where pressure data does not show clear distinction of gas and oil gradient in the reservoir. In the last example, we illustrate the use of reservoir fluid knowledge from AMG to characterizing the fluid variation across a field. The field applications demonstrate the success of the new method for conventional reservoirs, provided good-quality AMG data are available.
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.
Summary 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 because of early access, low cost, and a continuous reservoir fluid GOR for all reservoir zones. This paper releases further study results to predict other reservoir fluid properties in addition to GOR, which is essential for geo-operations, field development plans, and production optimization. Two approaches were selected to predict other reservoir fluid properties. As illustrated by the reservoir fluid density example, we developed machine learning models for individual reservoir fluid properties for the first approach, similar to the GOR prediction approach in the previous paper. As for the second approach, instead of developing many machine learning models for individual reservoir fluid property, we investigated the essential properties for equation of state (EOS) fluid characterization: C6 and C7+ composition and the molecular weight and density of the C7+ fraction. Once these properties are in place, the entire spectrum of reservoir fluid properties can be calculated with the EOS model. The results of reservoir 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 similar to the 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 less than the laboratory measurements but acceptable as the early phase estimations. The reservoir fluid properties predicted from the EOS model are similar to the predictions from individual machine learning models. We applied the field measured AMG data into the reservoir fluid property models and achieved good results, as illustrated by the reservoir fluid density example. The previous paper completed the methodology to predict all reservoir fluid properties based on AMG data. This work paves the way to generate a complete reservoir fluid log for all relevant reservoir fluid properties while drilling. The method has a significant business impact, providing full coverage of reservoir fluid properties along the well path in the early drilling phase. The advantage of providing reservoir fluid properties in all reservoir zones while drilling far outweighs the limitation of somewhat reduced reservoir fluid property accuracy.
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