Applied Novel Quality Check Method for PVT Data with High Impurities Using Various Samples from Malaysian Fields

PVT analysis of reservoir fluid samples provides essential information for determining hydrocarbon in place, depletion strategy, and hydrocarbon flowability. Hence, quality checking (QC) is necessary to ensure the best representative sample for further analysis. Recently, a novel tool based on Equation of State (EOS) was introduced to tackle the limitation of the Hoffmann method for surface samples with high impurities and heavier components. This paper presents comprehensively evaluating a novel EOS-based method using various PVT data from Malaysian fields. Numerous PVT separator samples from 30 fields with various reservoir fluids (Black Oil, Volatile, and Gas Condensate) were carried out and evaluated. The impurities contain a wide range of up to 60%. The 2-phase P-T (pressure and temperature) diagram of each oil and gas phase before recombination was calculated using PVT software based on Equation of State (EOS). The 2-phase P-T diagram was created and observed the intersection point as calculated equilibrium at separator conditions. Once it is observed and compared with written separator condition in the laboratory report and observed its deviation. Eventually, the result will be compared with the Hoffmann method. The Hoffmann method is well-known as a traditional QC method that was initially developed using gas condensate PVT data to identify possible errors in measured separator samples. If the sample has high impurities and/or heavier components, the Hoffmann method will only show a straight line to the lighter components and those impurities and heavier components will be an outlier that engineers will misinterpret that it has errors and cannot be used for further analysis such PVT characterization. The QC using EOS-based were conducted using actual fields data. It shows potential as novel QC tools but observed only less than 10% of data with complete information that can meet intersection points located precisely similar with reported in the laboratory. There is some investigation and evaluation of the EOS-based QC method. First, most of the molecular weight of the heavier fluid composition of gas and oil phase was not reported or used assumptions especially when its mole fraction is not zero. Second, properties of heavier components of the oil phase (molecular weight and specific gravity) were not measured and assumed similar as wellstream. Third, pressure and temperature data are inconsistent between the oil and gas phase at the separator condition. This study can provide improvement in laboratory measurement quality and help engineers to have a better understanding of PVT Report, essential data requirements, and assumptions used in the laboratory. Nevertheless, the Hoffmann method can be used as an inexpensive QC tool because it can be generated in a spreadsheet without a PVT software license. Both combination techniques can provide a comprehensive evaluation for separator samples with high impurities before identifying representative fluid for further analysis.

4D Seismic Detectability in a Brazilian Pre-Salt Carbonate Field

This paper is dedicated to a pre-salt carbonate field located within the Santos Basin, Brazil, comprising thick Aptian reservoirs interspersed with igneous rocks. One of the main challenges for reservoir management is the surface constraint on the gas, as all of the produced gas will have to be reinjected and can be miscible with the in-situ hydrocarbons. The recovery mechanism selected is mainly WAG (water alternating gas) injection, with both producers and injectors equipped with intelligent completions using Inflow Control Valves (ICVs). A 4D seismic monitoring survey is planned to delineate gas and water fronts in reservoir flow units about 10m thick, providing critical information to help piloting a planned 6-month WAG cycle for improved recovery. Seismic imaging is challenging in this case and 4D signal is expected to be weak (±2% dIp/Ip). We propose here, a methodology, based on a 1-D Gassmann fluid substitution model at wells (only limited reservoir fluid PVT data available) to rapidly answer the following pertinent questions as posed by the asset team in charge of the field: From a phenomenological stand-point and neglecting some possible processing, imaging and acquisition challenges, will 4D data (post 4D inversion) detect a gas streak from an injector to a producer? What is the 4D seismic detection limit based on reservoir thickness? What kind of seismic acquisition will assure this detectability? Under the assumptions made in this work, this methodology shows that a permanent system of acquisition seems to be a fit-for-purpose technology for detectability. Further work is however recommended using full complement of a 3D static and dynamic simulation model coupled with a complete fluid PVT model in order to assess more complex 3D dynamic interactions between the injectors and producers.

Evaluating Precision of Annular Pressure Buildup (APB) Estimation Using Machine-Learning Tools

Summary Annular pressure buildup (APB) is caused by heating of the trapped drilling fluids (during production), which may lead to burst/collapse of the casing or axial ballooning, especially in subsea high-pressure/high-temperature wells. The objective of this paper is to apply machine-learning (ML) tools to increase precision of the APB estimation, and thereby improve the fluid and casing design for APB mitigation in a given well. The APB estimation methods in literature involve theoretical and computational tools that accommodate two separate effects: volumetric expansion [pressure/volume/temperature (PVT) response] of the annulus drilling fluids and circumferential expansion (and corresponding mechanical equilibrium) of the well casings. In the present work, ML algorithms were used to accurately model “fluid density = f(T, P)” based on the experimental PVT data of a given fluid at a range of (T, P) conditions. Sensitivity analysis was performed to demonstrate improvement in precision of APB estimation (for different subsea well scenarios using different fluids) using the ML-basedmodels. This study demonstrates that, in several subsea scenarios, a relatively small error in the experimental fluid PVT data can lead to significant variation in APB estimation. The ML-based models for “density = f(T, P)” for the fluids ensure that the cumulative error during the modeling process is minimized. The use of certain ML-based density models was shown to improve the precision of APB estimation by several hundred psi. This advantage of the ML-based density models could be used to improve the safety factors for APB mitigation, and accordingly, the work may be used to better handle the APB issue in the subsea high-pressure/high-temperature wells.

A Comparative Analysis of the Prediction of Gas Condensate Dew Point Pressure Using Advanced Machine Learning Algorithms

Dew point pressure (DPP) is a key variable that may be needed to predict the condensate to gas ratio behavior of a reservoir along with some production/completion related issues and calibrate/constrain the EOS models for integrated modeling. However, DPP is a challenging property in terms of its predictability. Recognizing the complexities, we present a state-of-the-art method for DPP prediction using advanced machine learning (ML) techniques. We compare the outcomes of our methodology with that of published empirical correlation-based approaches on two datasets with small sizes and different inputs. Our ML method noticeably outperforms the correlation-based predictors while also showing its flexibility and robustness even with small training datasets provided various classes of fluids are represented within the datasets. We have collected the condensate PVT data from public domain resources and GeoMark RFDBASE containing dew point pressure (the target variable), and the compositional data (mole percentage of each component), temperature, molecular weight (MW), MW and specific gravity (SG) of heptane plus as input variables. Using domain knowledge, before embarking the study, we have extensively checked the measurement quality and the outcomes using statistical techniques. We then apply advanced ML techniques to train predictive models with cross-validation to avoid overfitting the models to the small datasets. We compare our models against the best published DDP predictors with empirical correlation-based techniques. For fair comparisons, the correlation-based predictors are also trained using the underlying datasets. In order to improve the outcomes and using the generalized input data, pseudo-critical properties and artificial proxy features are also employed.

A Novel Approach to Determine Reservoir Fluid Composition from Separator Test Data

High quality composition and PVT data can directly improve a wide range of upstream and downstreamengineering calculations. The quality of PVT Black oil experiments depends on awide variety of factors that includes type of PVT system, pressure-temperature conditions, stability and composition. The objective of this study is to emphasize the need to use material balance calculations obtainedfrom separator test data to back-calculate the reservoir composition andvalidate it against the original reservoir fluid composition. The methodologies employed in this study involve the following steps;

A simple approach to dynamic material balance for a dry-gas reservoir

The dynamic material balance methodology can be used to estimate gas initially-in-place using only production and PVT data. With this methodology, reservoir pressure is obtained without requiring the well to be shut in; it is therefore superior to the static material balance method since there is no loss of production. However, the technique requires iterative calculations and numerical integration of gas pseudotime and is quite complex to implement in practice. A simpler and equally accurate methodology is proposed in this study. It requires only production and PVT data and also does not rely on a shut-in pressure survey. In addition, it requires neither iterative calculations nor numerical integration of gas pseudotime. The results of the analysis include gas initially-in-place and gas productivity index, and can easily be extended to production forecasting. Gas initially-in-place is evaluated with a high degree of reliability. The methodology is successfully tested with two simulated cases and one field case, giving high-accuracy results.

101 Measurement of Tapping During the Interstimulus Interval as a Validation Metric for the 3-Minute Psychomotor Vigilance Test

Introduction The Psychomotor Vigilance Test is a well-validated measure of sustained attention used to assess daytime alertness in sleep research studies.1 It is commonly used in a variety of research settings due to its high sensitivity to sleep loss and absence of learning effects,2 making it an ideal tool to assess objective alertness. As some types of sleep research transition out of controlled laboratory environments, tools like the PVT require modification to maximize their reliability. The validation of the 3-minute version (PVT-B) against the 10-minute PVT is an example of this modification.3 However, considerable work is needed to improve trust in the utility of the PVT-B in and outside of traditional laboratory settings. Methods We carefully analyzed data from a mobile-based version of the PVT-B, noting responses that occurred during the interstimulus interval which were termed “wrong taps.” Wrong taps indicated that participants were not performing the task as instructed. In some cases, wrong taps occurred across multiple trials of the same PVT block, indicative of participants repeatedly tapping the screen throughout the task to minimize response times. A comprehensive examination of wrong taps was carried out in order to identify instances where this pattern emerged. Results A total of 1,338,538 PVT-B trials from 7,028 participants were examined to determine the number of wrong taps present across all trials. While 91.7% of PVT-B trials were free of wrong taps, 8.3% of PVT-B trials contained 1 or more wrong taps and 5.2% contained 2 or more wrong taps. It appears that a maximum of one wrong tap per trial is acceptable and trials containing 2 or more should be excluded to maximize PVT data quality. Conclusion Utilizing a metric like wrong taps can help identify individuals taking the PVT-B who are tapping the screen multiple times prior to stimulus display. Closely examining this metric can help to ensure the validity of PVT-B administrations. Two possible uses of the metric could be to provide feedback during training trials and to remove trials where this strategy was employed. Support (if any) This analysis was supported by the VA San Diego Healthcare System Research Service.

104 The influence of self-reported distractions on the Psychomotor Vigilance Task (PVT) in laboratory and field environments

Introduction The Psychomotor Vigilance Task (PVT) is a measure of vigilant attention that is commonly used in laboratory environments to assess the neurobehavioral impact of sleep loss and circadian misalignment. The PVT has been increasing in popularity for use in field environments; however, the potential for distraction is higher in the field compared to the lab. It is unclear how distractions experienced by individuals taking the PVT in the real world may influence reaction time metrics. We investigated the influence of self-reported distraction on PVT outcomes across laboratory and field environments. Methods We examined PVT data from five studies including short (n=36 participants, 3799 PVTs) and long-haul (n=75 participants, 3282 PVTs) airline personnel, control center personnel (n=5 participants, 96 PVTs), and healthy individuals who participated in a study involving at-home and laboratory assessments (n=12 participants, 486 and 310 PVTs). Individuals in all of the studies were asked to complete the five-minute NASA PVT at least three times daily. Participants were asked to indicate the number of distractions they experienced immediately after each PVT. Mean PVT reaction time (RT) and number of distractions were computed for each study and overall. Results Participants reported more distractions in field environments compared to the lab (short-haul=1.29 +/- 1.48, long-haul=0.66 +/- 1.07, control-center=1.20 +/- 1.37, at-home=0.86 +/- 1.36, laboratory=0.46 +/- 1.07) Across all studies, we found that PVT RT slowed as self-reported distractions increased (all studies combined: 0 distractions=PVT RT 275.7ms; 1=285.0ms; 2=304.0ms; 3=322.9ms; >4=408.6ms). These findings were similar for healthy participants completing PVTs at home (0 distractions=286.4ms; 1=309.9ms; 2=328.3ms; 3=369.8ms; >4=385.1ms) but were less consistent during in-lab assessments (0 distractions=278.7ms; 1=316.2ms; 2=396.2ms; 3=370.4ms; >4=354.4ms). These findings were similar for other PVT outcomes. Conclusion Participants reported more distractions in field environments compared to the laboratory. Our findings suggest that the number of distractions that individuals report experiencing while taking a PVT increases the reaction time registered by the device. Researchers should collect information about distractions during the PVT and should be aware that distractions may influence the recorded PVT reaction time. Support (if any) NASA Airspace Operations and Safety Program, System-Wide Safety Project