Advanced Mud Gas Research Articles

Recognition of Artificial Gases Formed during Drill-Bit Metamorphism Using Advanced Mud Gas

Drill-bit metamorphism (DBM) is the process of thermal degradation of drilling fluid at the interface of the bit and rock due to the overheating of the bit. The heat generated by the drill when drilling into a rock formation promotes the generation of artificial hydrocarbon and non-hydrocarbon gas, changing the composition of the gas. The objective of this work is to recognize and evaluate artificial gases originating from DBM in wells targeting oil accumulations in pre-salt carbonates in the Santos Basin, Brazil. For the evaluation, chromatographic data from advanced mud gas equipment, drilling parameters, drill type, and lithology were used. The molar concentrations of gases and gas ratios (especially ethene/ethene+ethane and dryness) were analyzed, which identified the occurrence of DBM. DBM is most severe when wells penetrate igneous and carbonate rocks with diamond-impregnated drill bits. The rate of penetration, weight on bit, and rotation per minute were evaluated together with gas data but did not present good correlations to assist in identifying DBM. The depth intervals over which artificial gases formed during DBM are recognized should not be used to infer pay zones or predict the composition and properties of reservoir fluids because the gas composition is completely changed.

Real-Time Fluid Identification From Integrating Advanced Mud Gas and Petrophysical Logs

Real-Time Fluid Identification From Integrating Advanced Mud Gas and Petrophysical Logs

Holistic Evaluation of Reservoir Oil Viscosity in Breidablikk Field – Including Mud Gas Logging Approach

Breidablikk is a green field on the Norwegian Continental Shelf that just started the preproduction drilling of 23 wells in two structures. We have two reservoir fluid samples from exploration wells in each structure with relatively high viscosity of 4 and 8 cP, respectively. Our dynamic reservoir simulations on the Breidablikk Field indicate that any change in the viscosity in each direction can lead to a 20 to 30% difference in oil recovery. Therefore, updating our reservoir models with the viscosity distribution in the field along with the drilling activities is important. Currently, our models assume homogeneous reservoir oil viscosities across each structure. In this study, our primary aim is to conduct a holistic evaluation of the reservoir oil viscosity, using multiple methods to determine the most effective approach for qualitatively mapping the oil viscosity across the field, distinguishing between the low- and high-viscosity regions. The technologies chosen for this assessment are standard mud gas data, advanced mud gas data, and analysis of oil extracts from cuttings, given they have previously demonstrated their capability to estimate fluid properties while drilling or within a limited time frame, as evidenced by the work of Cutler et al. (2022). The methods were compared using pressure/volume/temperature (PVT) measurements as a benchmark. As of today, this method is considered the most reliable to obtain reservoir fluid properties, and in consequence, these measurements serve as the reference viscosity values in the study. The results of our analysis in Breidablikk show that an approach based on advanced mud gas data provide an oil quality classification that distinguishes between high- and low-viscosity reservoir oils, using the ethane/n-pentane ratio as the best parameter correlated to reservoir oil viscosity in Breidablikk. The threshold for the two viscosity regions is identified from a reservoir fluid database from the Breidablikk-Grane area, and the oil viscosity region estimated from advanced mud gas data agrees well with the PVT measurements. The viscosity estimation using a standard mud gas approach based on methane to propane compositions indicates that this technology cannot correctly differentiate between low- and high-viscosity region wells in the Breidablikk Field. Hence, it is not recommended. Further findings from our analysis indicate that the utilization of oil-based mud, combined with a high drilling speed, significantly affects the quality of the cuttings in Breidablikk. Consequently, the application of traditional geochemical analysis methods on cutting extracts is challenging. Therefore, this method is not recommended for the qualitative identification of the viscosity region of a given well. Benchmarking all available technologies allows us to select a real-time, reliable, and cost-efficient method to qualitatively estimate reservoir oil viscosity in Breidablikk. The selected method is field-specific and not general for other heavy oil fields. In summary, providing an accurate reservoir oil viscosity mapping at an early stage in field development plays a crucial role in the further optimization of drilling targets and ultimately leads to improved oil recovery (Halvorsen et al., 2016; Maraj et al., 2021).

Technology Focus: Reservoir Surveillance (September 2021)

It is not unusual to compare a team of subsurface professionals to a team of detectives piecing together a sequence of events to solve a crime. To make sense of what is happening in a hydrocarbon reservoir, subsurface teams, like detectives, typically have incomplete, sparse data sets, sampled at different points in time and space. The data only provide a partial picture of what has happened and what is likely to happen in the future. In either case, surveillance is an essential tactic to build a mental model of the situation. Fortunately, both detectives and subsurface teams have growing surveillance toolboxes to help fill information gaps and narrow the range of possible scenarios. In the oil and gas industry, an endless set of questions can be asked to characterize the state and history of a hydrocarbon reservoir. Teams need to understand the capability of the reservoir to store fluids, stresses acting on the reservoir, what fluids exist and how they interact with each other and the rock, and how fluids are moving (or are likely to move) through the reservoir. Information, however, is rarely free, and different surveillance tools provide varying qualities of information, so it is essential for subsurface professionals to choose wisely in terms of which problems to solve and which tools to pull out of the toolbox. Ultimately, we need to apply the right tools to the right problems to maximize the value of the information we gather. In this feature, we will explore innovative approaches to help better understand the stress state of the reservoir, interactions between different fluids and rocks, and how to track the movement of specific fluid components throughout the reservoir. To do so, the authors of the papers highlighted in this month’s feature apply advanced log data analysis, experimental laboratory work, and compositional reservoir simulation, key tools that every subsurface team should have in its toolbox. Recommended additional reading at OnePetro: www.onepetro.org. SPE 201679 - A Fast Method To Estimate the Correlation Between Confining Stresses and Absolute Permeability of Propped Fractures by Faras Al Balushi, The Pennsylvania State University, et al. SPE 202224 - Downhole Surveillance During the Well Lifetime Using Distributed Temperature Sensing by Ludovic Paul Ricard, CSIRO, et al. SPE 201635 - Predicting Reservoir Fluid Properties From Advanced Mud Gas Data by Tao Yang, Equinor, et al.

Predicting Reservoir Fluid Properties from Advanced Mud Gas Data

SummaryIn 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.