Development of reliable models for hydrocarbon-in-place and water saturation estimation requires knowledge about wettability of mudrocks and the parameters (including rock properties and reservoir condition) affecting it. A significant volume fraction of organic-rich mudrocks is composed of kerogen. Therefore, wettability of kerogen affects the overall wettability of organic-rich mudrocks. The chemical composition and structure of kerogen varies with kerogen type and thermal maturity, which affects the surface properties of kerogen such as wettability. In a recent publication, we demonstrated using experimental techniques that kerogen could be water-wet at low thermal maturities and oil-wet at higher thermal maturities. However, the impacts of kerogen type and reservoir temperature/pressure conditions on kerogen and mudrock wettability is yet to be quantified. Therefore, the objectives of this paper include (i) quantifying the impacts of kerogen molecular structure and composition on water adsorption capacities, (ii) quantifying the impacts of reservoir pressure and temperature on water adsorption capacity of kerogen using molecular dynamics (MD) simulations.
In order to achieve the aforementioned objectives, we use a combination of molecular dynamics simulations and experimental work. The inputs to the molecular dynamics simulations include realistic models of kerogen, which are condensed to porous kerogen structures. Water molecules are filled in kerogen pore structure and MD simulation is performed. The outputs of the simulations include radial distribution function (RDF), and adsorption isotherms of water on kerogen for different kerogen types, thermal maturities, and temperature conditions. The adsorption processes are modelled for pressure and temperature conditions ranging from 0 to 35 MPa and 320 to 370 K, respectively. The outcomes of molecular dynamics simulations demonstrated that the water adsorption capacities of kerogen vary significantly with kerogen type, thermal maturity, and temperature and pressure conditions. The RDF results showed that the water adsorption capacity decreased from type I to type III kerogen. The water adsorption capacity of kerogen was found to increase by 128% with 38% increase in oxygen content. The increase in the adsorption capacity was attributed to the strong attraction between oxygen containing functional groups in kerogen and water. The adsorption isotherms of water and kerogen samples showed that the water adsorption capacity decreased by 0.19 mmol/g as the temperature increased from 320 K to 370 K. The average water adsorption capacity of kerogen was found to increase by 20% with increase in pressure by 34 MPa. The results obtained from molecular dynamics simulations were found to be in good agreement with experimental results. The results of this paper can be used to predict the adsorption capacities of any kerogen with the availability of geochemical information. This important property of kerogen is required for estimating kerogen wettability and can enhance understanding of fluid-flow mechanisms in organic-rich mudrocks.
Cui, Xiaona (Texas A&M University and Northeast Petroleum University) | Song, Kaoping (China University of Petroleum - Beijing) | Yang, Erlong (Northeast Petroleum University) | Jin, Tianying (Texas A&M University) | Huang, Jingwei (Texas A&M University) | Killough, John (Texas A&M University) | Dong, Chi (Northeast Petroleum University)
The phase behavior shifts of hydrocarbons confined in nanopores have been extensively verified with experiments and molecular dynamics simulations. However, the impact of confinement on large-scale reservoir production is not fully understood. This work is to put forward a valid method to upscale the pore-scale fluid thermodynamic properties to the reservoir-scale and then incorporate it into our in-house compositional simulator to examine the effect of confinement on shale reservoir production.
Firstly, a pore-scale fluid phase behavior model is developed in terms of the pore type and pore size distribution (PSD) in the organic-rich shale reservoir using our modified Peng-Robinson equation of state (PR-C EOS) which is dependent on the size-ratio of fluid molecule dynamic diameter and the pore diameter. And the fluid composition distribution and PVT relation of fluids in each pore can be determined as the thermodynamic equilibria are achieved in the whole system. Results show that the initial fluid composition distribution is not uniform for different pore types and pore sizes. Due to the effect of confinement, heavier components are retained in the macropore, and lighter components are more liable to accumulate in the confined nanopores. Then an upscaled equation of state is put forward to model the fluid phase behavior at the reservoir-scale based on our modified PR-C EOS using a pore volume-weighted average method. This upscaled EOS is validated with the pore-scale fluid phase behavior simulation results and can be used for compositional simulation. Finally, two different reservoir fluids from the Eagle Ford organic-rich shale reservoir are simulated using our in-house compositional simulator to investigate the effect of confinement on production. In addition to the critical property shift which can be described by our upscaled PR-C EOS, capillary pressure is also taken into account into the compositional simulation. Results show that the capillary pressure has different effects on production in terms of the fluid type, leading to a lower producing Gas/Oil ratio (GOR) for black oil and a higher GOR for gas condensate. Critical property shift has a consistent effect on both the black oil and gas condensate, resulting in a lower GOR. It should be noted that the effect of capillary pressure on production is suppressed for both fluids with the shifted critical property.
Robust downhole fluid analysis highly relies on quality measurements and data mapping from various optical sensors implemented into modern formation testers. The direct modeling used to determine a multivariate correlation between optical sensor responses and diverse fluid compositions or properties is often cost-prohibitive with sensor-based calibration. This paper presents a novel method based on concatenated optical computing neural networks (COCN), which link sensor-specific signal transformation to generic fluid characterization through validated data mapping.
The COCN models are built separately in the in-house laboratory using optical data transformation networks and multi-analyte fluid characterization networks. To evaluate the uncertainty of sensor signal mapping, additional sensor data prediction networks, which exchange inputs and outputs of sensor data transformation networks to provide reverse data mapping, can be built to produce the simulated sensor responses. The actual sensor measurements are then compared to the simulated sensor responses to validate the optical signal transformation. The degree of matching can be used to identify issues associated with the quality of the sensor measurements and the range of calibration and testing data, and to select the transformation networks in ruggedizing predictions of fluid compositions and properties.
This paper presents the method used and case studies to exemplify the application of the proposed approach for reliable fluid sample characterization using laboratory and field measurement data. Calibrating sensor-independent fluid predictive networks on a large database usually pertains to low uncertainty because the analyzed measurements can be collected from diverse reservoir fluid samples. The construction of data transformation networks, however, is only realistic by using a small number of reference fluids because of their high calibration cost, which conversely challenges the quality of data mapping with new field sensor measurements. In this study, sensor data transformation networks and sensor data prediction networks form two groups of mutually complementing models that can validate one another, making the quality of data mapping observable before applying fluid characterization models. The results of the case studies demonstrate that the consistency between the actual and simulated sensor responses is a reasonable performance index in the prediction of fluid compositions and properties. The laboratory and field examples also justified the importance of using simulation data appropriately to overcome the limitation of sensor measurement data in the calibration of transformation networks.
Zheng, Shuang (The University of Texas at Austin) | Manchanda, Ripudaman (The University of Texas at Austin) | Gala, Deepen (The University of Texas at Austin, Now with ExxonMobilUpstream Research Company) | Sharma, Mukul (The University of Texas at Austin)
Mitigating the negative impact of frac-hits on production from parent and child wells is challenging. In this work, we show the impact of parent well depletion and repressurization on the child well fracture propagation and parent well productivity in different US shale reservoirs. By repressurizing the parent well, we do not imply repressurization of the entire depleted reservoir. By repressurizing the parent well, we imply pressurization of only the near fracture regions. Our goal is to develop a method to better manage production/injection in the parent well and stimulation operations in the child well to minimize frac-hits and improve oil and gas recovery.
We have developed a fully implicit, 3-D, parallelized, poroelastic, compositional, reservoir-fracture simulator to seamlessly model fluid production/injection (water or gas) in the parent well and model propagation of multiple fractures from the child well (
We have analyzed the effects of drawdown rate and production time in three shale plays: Permian (oil), Eagle Ford (volatile oil/gas condensate) and Haynesville (dry gas) reservoirs. The results show that different reservoir fluids and drawdown strategies for the parent wells result in different stress distributions in the depleted zone and this affects the child well fracture propagation. We studied different strategies to repressurize the parent well by varying the injected fluids (gas vs. water), pre-load fluid volumes, etc. It was found that frac-hits can be avoided if the fluid injection strategy is designed appropriately. In some poorly designed pre-loading strategies, frac-hits are still observed. Lastly, we analyzed the impact of pre-loading on the parent well productivity. When water was used for pre-loading, we observed water blocking in the reservoir that caused damage to the parent well. However, when gas was injected for pre-loading, the oil recovery of the parent well was observed to increase.
We present, for the first time, fully compositional geomechanical simulations of child well fracture propagation around depleted parent wells. We study the impact of parent well production reservoir fluid, etc. on child well fracture propagation. Fluid injection (pre-loading) strategy in the parent well and subsequent avoidance of frac-hits is also modeled. Such simulations of parent-child well interactions provide much-needed quantification to predict and mitigate the damage caused by depletion and frac-hits.
After a successful decade of exploration and development activities in major tight/shale reservoirs, the industy now has access to incredible sets of data, modeling tools, and technologies for multi-fractured horizontal well (MFHW) completion. A review of the available data and models shows that performance of a MFHW is governed by hydraulic facture properties (dimension, conductivity, and distribution) and reservoir fluid and rock characteristics (reservoir fluid properties, and rock storage and flow capacities). Workflows are required to link the characterization attempts (reservoir and MFHW), learnings from completion expriments, modeling approaches (reservoir and fracture modeing) and pettern recognition exercises (relationship between well performance metrics and the governing parameters).
In the current study, an interative workflow is proposed for design and optimization of MFHW completion based on a mixed-method approach combining three major paradigms: experiments, modeling, and data science. Each cycle of the workflow starts with data gathering and characterization of reservoir fluid and rock, followed by reservoir and fracture modeling, statistical analysis, updated design, economc analysis, and ends with implementation, monitoting and data analysis. The first cycle of the workflow is the most time-consuming and tedious one which requires a great deal of discussions and instructions.
The proposed workflow is tried on a population of Montney gas condensate wells. Rate-transient analysis (RTA) and numerical reservoir modeling were applied to a group of 16 Monteny gas condensate wells with detailed daily production and flowing pressure data. Further, a simplified RTA-based approach and statistical analysis were applied to more than 90 Montney gas condensate wells (from the same region) with publically available production data.
A new design with optimized completion paramteres is obtained from the results of RTA, numerical reservoir modeling, statistical and ecnomic analyses. The new design is applied to six new wells in the same area. The average performance of the new wells is reasonably close to the predicted performance by the proposed workflow. The workflow is believed to optimize the well performance, save the operator millions of dollars through optimization, and give the management and technical teams confidence in the next phase of corporate planning.
The method for modeling of a multilateral well design that is completely independent on the simulation grid and fluid properties is proposed. The method takes into account friction in the lateral branches and crossflow between them. Well parameters, such as trajectory, perforation intervals, roughness and diameter, are directly used to calculate pressure distribution along the wellbore at the current fluid composition and tubing head pressure (THP).
Well connections with grid blocks in a finite volume approximation for dynamic model should be created. The automatic creation of the well connections during dynamic simulation based on specified well trajectory and completion intervals is proposed. The connection factor is suggested to be calculated based on length of completion intersection with the block, trajectory direction and rock properties during the run time. To calculate pressure drop on well track intervals between connections and the well track intervals between top completion and tubing head the well-known correlations are utilized. The correlations are used for the current fluid composition in the wellbore in each connection using information for well trajectory, roughness and diameter.
Such an approach makes it possible to get rid of the use of the tabulated bottomhole pressure (BHP) as a function of tubing head pressure for a number of phase compositions. Such traditional use of phase compositions gives a non-physical response in compositional models, where the component composition of the product varies significantly throughout the life of the field. Usage of real coordinates (x, y, z) for setting well trajectory and perforation intervals, instead of the traditional grid block numbers (i, j, k), allows to calculate layer intersection, connection factors and pressure distribution along wellbore with arbitrary changes in the dynamic model grid, for example, when introducing local grid refinement or dynamic grid and rock properties variation used to describe hydraulic fracturing.
The proposed method is successfully used for modeling of a multilateral well design in dynamic simulation. The results of such dynamic simulation are consistent with the real samples from reservoir.
Data from seismic to production is integrated to build models to provide estimations of parameters such as petroleum volumetrics, pressure behavior, and production performance (
Reservoir dynamic simulation is the most applied process that integrates all reservoir data, where an Equation of State (EOS) is coupled with the objective to estimate the fluid thermodynamic state at each computational step. The simulation consists of iterative mathematical computations in which the reservoir-defined conditions at the previous time step is an input to determine the properties at the next and subsequent time steps. The calculated pressure is a fundamental variable in each time step, which means that a representative and high level of confidence Pressure Volume Temperature (PVT) model is required to avoid scale-up of errors resulting from fluid pressure estimation.
A PVT modeling includes three main stages: Fluid sample and data acquisition Laboratory analysis and fluid characterization The EOS model.
Fluid sample and data acquisition
Laboratory analysis and fluid characterization
The EOS model.
The emphasis in this work is on the EOS model, which is the fluid model used for the simulation process. The objective of this work is to analyze the main uncertainties associated with typical EOS modeling and defining the level of confidence of these EOS approaches. In this work, some of the most-used approaches for EOS modeling are reviewed. An assessment of these methods is also provided based on their application to actual petroleum fluids with the objective of defining their statistical level of confidence.
First, the study analyzes the sources of critical uncertainties in a PVT EOS model. Second, a statistical number of PVT laboratory studies of petroleum fluids is used to determine the level of confidence of four approaches that are based on the two well-known Peng-Robinson and Soave-Redlich-Kwong EOS. Third, statistical analysis is performed to determine the level of confidence of the different methods. Fourth, a correlation to determine the optimal number of pseudo-components is defined. These steps include: Characterization of fluid and heavy components Tuning Lumping.
Characterization of fluid and heavy components
As a result of this study, one can conclude: The level of confidence of the four analyzed approaches The significance of the difference between the analyzed methods A correlation to determine the optimal number of pseudo-components.
The level of confidence of the four analyzed approaches
The significance of the difference between the analyzed methods
A correlation to determine the optimal number of pseudo-components.
In this work, a statistical analysis over some of the most-used EOS modeling approaches and on a set of petroleum fluid PVTs was performed to determine the level of confidence of four EOS modeling methods. In addition, a correlation was introduced for
The technical challenges imposed by tight well spacing and fracture interactions have become a focal point of recent earnings calls between investors and the leaders of several shale producers. The picture of the future is becoming clearer, and there are fewer oil wells in it. A close look at hundreds of feet of fractured core samples suggest that new fracture models are needed to simulate complicated reality.
When it came to decide where to collect a critical sample of fractured rock, a new method for turning microseismic data into a heat map designed to display the most intense fracturing activity was considered. By measuring which tests best predicted the fractures observed at the Hydraulic Fracturing Test Site, Laredo Petroleum developed a method it hopes will improve fracture modeling in other places. A close look at hundreds of feet of fractured core samples suggest that new fracture models are needed to simulate complicated reality.
An Excel-based tool was developed that uses cubic-equation-of-state (EOS) and thermodynamic electrolyte-chemistry modeling to assess sour-production streams. The deepwater industry uses technology qualification (TQ) as a tool to determine which safety barriers are needed, and what level of testing is required. Even without failure data from the field, a method has been developed to quantify the integrity of various components.