Moreno Ortiz, Jaime Eduardo (Schlumberger) | Gossuin, Jean (Schlumberger) | Liu, Yunlong (Schlumberger) | Klemin, Denis (Schlumberger) | Gurpinar, Omer (Schlumberger) | Gheneim Herrera, Thaer (Schlumberger)
Challenges on EOR process upscaling have been discussed extensively in the industry and effects of diffusion, dispersion, heterogeneity, force balance and frontal velocity -among others, recognized and qualified, along with the importance of understanding the numerical model finite difference equations and modeling strategy. Augmenting the upscaling complexity is the often-limited understanding/data on the EOR displacement at different scales (from micro to full field), including the EOR agent/rock/fluid interactions that is often available at the early stages of the EOR process de-risking.
A common denominator for the EOR process characterization and upscaling (along with the discretization of the displacement) is the non-uniqueness nature of the problem. As the complexity of numerical representation of the EOR process increases (thus increasing data characterization requirements), so does the number of plausible solutions and challenges when dealing with an otherwise incomplete dataset. Digital rock has evolved as a strong alternative to complement laboratory corefloods, allowing for EOR agent optimization on a high-resolution digital representation of the pore structure, detailed digital fluid model of both reservoir fluids and EOR agents and physical rock-EOR agent-reservoir fluid interaction, thus providing several calibration points to ensure the finite-difference model calibration and upscaling preserve the process behavior.
This paper discusses the use of digital rock solutions on the EOR deployment, particularly on translating the results to numerical finite difference models, addressing the inherent laboratory measurement uncertainty and proposing a fit-for-purpose multi-scale upscaling strategy that addresses both effects of heterogeneity and EOR agent characterization during the upscale process.
This paper addresses the challenges of chemical flooding upscaling, particularly polymer by using a real-life polymer injection case where digital rock, corefloods and more importantly pilot results are available to test and validate our observations. Using a polymer coreflood and digital rock results as input, numerical finite difference simulation models were built and calibrated to effectively reproduce the displacement physics observed on both digital rock and corefloods, digital flood results were used to bridge the laboratory-to-numerical model step by providing effective upscaled polymer properties as well as intrinsic rock properties such as relative permeability and capillary pressures, which are then taken through a series of multi-scale finite difference models to identify, validate and quantify upscaling requirements, addressing polymer deformation through pore throats and effective simulation viscosity. Digital rock is used to rank and resolve ambiguity on the finite difference model calibration by providing an otherwise rare opportunity to visualize the displacement in the 3D space. The analysis shed a new light on fluid-fluid and fluid-rock interaction at pore scale and enabled us to improve on the finite difference model generation and polymer properties.
Carlsen, Mathias Lia (Whitson AS) | Whitson, Curtis Hays (Whitson AS, NTNU) | Alavian, Ahmad (Whitson AS) | Martinsen, Sissel Øksnevad (Whitson AS) | Mydland, Stian (Whitson AS, NTNU) | Singh, Kameshwar (Whitson AS) | Younus, Bilal (Whitson AS) | Yusra, Ilina (Whitson AS)
In this paper we emphasize the duality of fluid sampling: (1)
To make a comprehensive assessment of fluid sampling in tight unconventionals, reservoir fluids ranging from black oils to gas condensates have been studied. For a wide range of fluid systems, a compositional reservoir simulator has been used to assess two main scenarios: (1) an initially undersaturated (single-phase) fluid system, and (2) initially saturated (two-phase) fluid system. To quantify how collected surface samples change with time, three properties are studied as functions of time: (1) saturation pressure and type (dewpoint | bubblepoint), (2) producing gas/oil ratio (GOR), and (3) stock-tank oil (STO) API. Observations of how these three properties change with time is used to help explain why elevated saturation pressures, greater than the initial reservoir pressure, often can be observed.
Rapid decline of the flowing bottomhole pressure (BHP | pwf), together with shut-in periods, makes it difficult to obtain in-situ representative samples in MFHW. For slightly undersaturated reservoirs, and saturated reservoirs, it may be impossible to obtain in-situ representative fluid samples because of the near-wellbore multiphase behavior. However, samples which are not in-situ representative can still be used to estimate original in-situ fluids using
This paper describes a trial project to evaluate autonomous inflow control device (AICD) technology to better manage water production in a large heavy oil field in Colombia. The Cajua block is part of the Rubiales field is in the Llanos basin of Eastern Colombia, and has reserves estimated at 7.5 billion barrels. One of the main production challenges is the high water cut, or BSW, driven by strong aquifer flow in the underlying sands of the Carbonera formation. Many wells experience early water break-through and must be produced above 95% BSW for long periods of time. Horizontal wells typically produce up to 8000 barrels per day of total fluid with electric submersible pump (ESP) on cold production, and do not utilize any thermal recovery methods. The loosely-consolidated sandstone reservoir has variable water saturation and permeability, which has continuously frustrated operators'’ attempts to manage water production ever since the Rubiales field was first brought online in the 1980's.
In late 2018, a three well pilot project was initiated to evaluate the ability of inflow control technology to manage water influx at the sandface of the horizontal completions. Three wells in the Cajua block were equipped with AICD screens and swellable packers to evaluate oil production and water cut. The AICD technology works by limiting water inflow based on fluid viscosity. Each segment, or compartment, of the horizontal wellbore is isolated by swellable packers, and the AICD creates a higher or lower drawdown on the reservoir depending on the fluid properties, favoring the inflow of high-viscosity heavy oil over the low-viscosity water.
The early production results show that AICD completions can effectively manage water production by delaying water break-through and restricting water inflow from the reservoir. Each of the three trial wells responded positively to the autonomous ICDs, allowing engineers to produce heavy oil wells more effectively with lower cumulative water volumes.
This project marks the first implementation in South America of AICD technology with rate-controlled production (RCP) valves to manage water production in a heavy oil field. It is also the second application worldwide, after Canada, to show that AICDs can effectively to manage water cut in a heavy oil, cold-production scenario.
The oil & gas industry uses production forecasts to make a number of decisions as mundane as whether to change the choke setting on a well, or as significant as whether to develop a field. As these forecasts are being used to develop cashflow predictions and value and decision metrics such as Net Present Value and Internal Rate of Return, their quality is essential for making good decision. Thus, forecasting skills are important for value creation and we should keep track of whether production forecasts are accurate and free from bias.
In this paper we compare probabilistic production forecasts at the time of the development FID with the actual annual production to assess whether the forecasts are biased; i.e., either optimistic, overconfident, or both.
While biases in time and cost estimates in the exploration & production industry are well documented, probabilistic production forecasts have yet to be the focus of a major study. The main reason for this is that production forecasts for exploration & production development projects are not publicly available. Without access to such estimates, the quality of the forecasts cannot be evaluated.
Drawing on the Norwegian Petroleum Directorates (NPD) extensive database, annual production forecasts, given at time of project sanction (FID), for 56 fields in the 1995 – 2017 period, have been compared with actual annual production from the same fields. The NPD guidelines specify that the operators should report the annual mean and P10/90-percentiles for the projected life of the field at the time of the FID; that is, the forecasts should be probabilistic. The actual annual production from the fields was statistically compared with the forecast to investigate if the forecasts were biased and to assess the financial impact of such biases.
This paper presents the results from the first public study of the quality of probabilistic production forecasts. The main conclusions are that production forecasts that are being used at the FID for E&P development projects are both optimistic and overconfident. As production forecasts form the basis for the main investment decision in the life of a field, biased forecasts will lead to poor decisions and to loss of value.
Metal expandable annular sealing systems were used in a 4 ½" completion as an effective high-pressure isolation method inside 6" open hole mudstone formation in the Foothills Basin of Colombia. Effective isolation proved to be historically difficult to achieve.
The operator was approached with a solid metal expandable sealing system with rotation capabilities as an annular barrier for a preferred cementless completion. The sealing system needed to be assembled on a full-bore liner able to deliver robust deployment with a high-pressure seal in a worse case washed-out scenario. The deployment of the system consisted of one annular barrier placed above and one annular barrier placed below the mudstone zone.
Following careful job planning with the operator, the rotationally capable completion was deployed without any incidents. To achieve pressure integrity to set the metal expandable annular barriers, a ball seat sealing system was incorporated to allow the system to be closed and the annular barriers to be set.
After putting the well onto the pipeline, the client recorded a 52% increase in their expected produc-tion from previous wells. Successful results were accomplished as effective isolation was achieved and enhanced production was obtained because of the effective stimulation. This paper overviews the appli-cation, design, implementation and results of the use of new metal annular sealing systems in a 4 ½" completion as an effective high-pressure isolation method inside a 6" open hole, drilled in fractured sandstone and mudstone formations.
Reservoir fluid characterization is critical to understanding the nature and phase behavior of reservoir fluids. This process has typically been undertaken using laboratory analyses, a time-intensive and costly process which also provides compositional data. Over time, correlations have been developed to predict the PVT properties of crude oil based on parameters such as solution gas-oil ratio, saturation pressure, viscosity, and density. These correlations have had shortcomings such as utilizing a leave-one-out approach, or recently, focused on non-inferable methods such as Neural Networks. This work utilizes compositional data, hitherto neglected in PVT correlations, as input into an inferable machine learning algorithm which can be used to predict PVT properties of crude oil from the Niger Delta basin.
Data containing bubble point pressure, solution gas-oil ratio, and oil formation volume factor alongside composition were obtained and used to develop models. Machine learning model training techniques such as data preprocessing, transformation and hyper-parameter tuning were undertaken. The elastic net regression algorithm utilizing a cross-validation approach was used to develop the models. This ensured an adequate bias-variance tradeoff.
The resulting models were compared with established correlations such as Standing & Katz. Upon statistical analyses performed comparably. The bubble point pressure model, solution gas-oil ratio, oil formation volume factor achieved R-squared value of 0.87, 0.95 and 0.84 respectively on the validation dataset. The models are expressed in the form of equations which can be used in petroleum engineering calculations or implemented in reservoir simulation software. By implementing this approach, a framework for utilizing machine learning for Petroleum Engineering problems which produces inferable results is established. Given potential discoveries in the Niger Delta, upon obtaining compositional data, these set of equations can be used to predict the reservoir crude oil PVT properties, leading to savings in time, cost, and effort, while obtaining actionable and accurate results.
Hwang, Jongsoo (The University of Texas Austin) | Sharma, Mukul (The University of Texas Austin) | Chiotoroiu, Maria-Magdalena (OMV Exploration & Production GmbH) | Clemens, Torsten (OMV Exploration & Production GmbH)
Several field cases reported that polymer injection in a horizontal well is a viable solution to increase oil recovery. The injectivity, however, may vary significantly depending on fluid, reservoir, and geomechanical conditions. Polymer injection without understanding these factors may lead to injectivity impairment, unswept zones, and fractures undesirable for the sweep. In this paper, we present a comprehensive viscoelastic polymer injectivity model for vertical and horizontal wells.
We developed a simulator to compute viscoelastic polymer injectivity by accounting for particle filtration, thermo-poro-elastic stress changes, fracture propagation, flow distributions among multiple layers, and viscoelastic polymer rheology. Simulation results clearly show that the contribution of shear-thickening characteristics on the polymer can have a large impact in un-fractured wells but have a much smaller impact in fractured injectors. The impact of geomechanical stress changes and subsequent induced fractures are also highlighted.
The model was then applied for a field case study to identify critical aspects needed to maintain high injectivity. Two field case wells are presented where water and viscoelastic polymer are injected for a vertical well and a horizontal well accessing the multi-layered reservoir respectively. For the two injectors, water was injected initially, and then HPAM polymer solution followed to improve oil recovery. Fracture growth and injection into a long horizontal lateral are the key factors that allowed the operator to maintain injectivity by reducing the Darcy velocity, shear rate, and shear-thickening zone. For a horizontal well, operating conditions are also identified by simulations to ensure matrix injection, which is the desired conformance and sweep improvement option.
The Young Member Outstanding Service Award recognizes contributions to and leadership in the public and community areas, as well as SPE, the profession, and the industry, by a member under age 36. Four awards were presented at the 2012 SPE Annual Technical Conference and Exhibition Annual Reception and Banquet, held 9 October in San Antonio, Texas. Do you think this award will have an impact on your professional life? This award to me is in recognition of my effort on behalf of and dedication toward SPE. An acknowledgment like this at such a high level provides the impetus and encouragement for me to carry on.
This paper presents a diagnostic workflow to understand and implement rock and fluid modeling in a diagenetically heterogeneous and hydrodynamically pressured Middle East carbonate field. The workflow allows interactive field data integration, provides guidance for reservoir property distribution and fluid contact generation in order to improve reserves and forecasting estimation. The workflow is useful to a reservoir modeler in QA/QC role and in this case it proves particularly applicable in an organization with constrained resources during the farm-in process. The workflow runs on numerical methods within the static model to avoid database discrepancy during the diagnostic process. Using the core (CCAL, SCAL), log and pressure database, the geoscientist can assess subsurface modeling outputs from the simplest to more complex deterministic scenarios. The process aims to minimize the discrepancy between data input and model output while continuously honoring the data, maintaining realistic correlations (e.g. between static permeability and water saturation) and respecting inherent uncertainty.
Using a data-rich Middle East carbonate reservoir, the pre- and post-diagnostic comparison of 3D modeled reservoir properties to the input data are demonstrated. Diagnostic steps have helped to understand potential subsurface scenarios and thus minimize the discrepancy post exercise. The value of the workflow is its ability to pinpoint the key uncertainties in rock and fluid modeling from the field’s vast dataset in a shorter diagnostic time. The application of the workflow in this carbonate reservoir case study increases the importance of geological and property driven rock type classification and its 3D distribution in matching the water saturation profile. This proved particularly challenging in this case study due to the field’s compartmentalization - fluid contact scenario.
Valencia, Juan D. (Universidad Nacional de Colombia, Exergy - Modeling and Analytics) | Mejía, Juan M. (Universidad Nacional de Colombia) | Ocampo, Alonso (GaStimTechnologies) | Restrepo, Alejandro (Equion Energía)
This paper address the numerical simulation of the chemically enhanced gas injection technology (ChEGas-EOR) at core and reservoir scales. In this technique, a liquid chemical solution, having engineered properties, is sprayed along with the gas stream. The mist travels through the wellbore and further introduced in the reservoir. Previous lab tests, pilot studies in light & intermediate oil reservoirs indicate that the application of CheGas-EOR allows for a reduction in operational costs, increases the chemical penetration radii and decreases the retention rate in the rock. However, the associated uncertainty is still too high to develop this process on a productive scale. In this work we use a developed phenomenological model to build a tool that assist in design and evaluation of Chemical Gas EOR operations aiming to reduce the uncertainties and optimize oil recovery.
We developed a mathematical model, based on the most important transport and surface phenomena. Non-equilibrium mass transfer between phases during the interception of the chemical solution droplets with the liquid phases. Active chemical concentration in miscible liquid phases is much lower than liquid-based chemical injection opperations. As a consequence, dissolution and adsorption rate of active chemicals with reservoir rocks are slow. The model is base on the extended black-oil model formulation coupled to local mass balance equations of active chemicals. Non-equilibrium mass transfer processes are represented with interception, dissolution and a first order kinetic sorption models.
The model was adjusted and then validated using experimental data from core-.floodint tests. Good agreement of the simulations results with experimental observations were obtained. The model can predict the relevant behavior of the disperse chemical injection in the gas phase in porous media. Also, well injections simulations at reservoir scale using the matched parameters from laboratory, reproduced pilot field results. Simulation experiments predict that the CheGasEOR process can increased substantially the oil recovery factor.
For the first time, a model for disperse chemical injection for EOR applications is developed and validated at core and reservoir scale. The simulation model allows the evaluation of this technology at different scales. Therefore, it is possible to use it to optimize operating conditions and perform sensitivity analysis for field applications.