Skrettingland, K. (Statoil ASA) | Ulland, E. N. (Statoil ASA) | Ravndal, O. (Statoil ASA) | Tangen, M. (Statoil ASA) | Kristoffersen, J. B. (Statoil ASA) | Stenerud, V. R. (Statoil ASA) | Dalen, V. (Statoil ASA) | Standnes, D. C. (Statoil ASA) | Fevang, Ø. (Statoil ASA) | Mevik, K. M. (Knutsen Subsea Solutions) | McIntosh, N. (Knutsen Subsea Solutions) | Mebratu, A. (Halliburton) | Melien, I. (Halliburton) | Stavland, A. (Intl Research Inst of Stavanger)
Declining oil production and increasing water cut in mature fields highlight the need for improved conformance control. Here we report on a successful in-depth water diversion treatment using sodium silicate to increase oil recovery at the Snorre field, offshore Norway, utilizing a new operational concept of using a stimulation vessel as a platform for the large-scale injection into a subsea well. A custom modified 35,000 DWT shuttle tanker was employed for the field pilot. This paper describes the vessel preparations and the large-scale interwell silicate injection operation. The operational aspects of the large-scale interwell silicate injection include; identification of injection vessel requirements, major vessel modifications, chemical logistic, general logistics to site, major equipment set-up on vessel, subsea connection, mixing and pumping schedules, onsite QC, and real time monitoring. Experience from these operations and lessons learned are included in this paper.
After the injection of approximately 400,000 Sm3 (113,000 Sm3 preflush, followed by 240,000 Sm3 of sodium silicate gelant and 49,000 Sm3 of postflush fluid) at injection rates up to 4,000 Sm3/d, the injection from the vessel was stopped and the well was put on regular seawater injection. Following more than two years of regular production, transient pressure measurements, tracer testing and water cut data are presented from the ongoing comprehensive data acquisition program. These results demonstrate clearly the achieved in-depth flow diversion through a delayed breakthrough of injected tracers and lower water cut in the relevant production well.
Although geochemical reactions are the fundamental basis of the alkaline/surfactant/polymer (ASP) flooding, their importance is commonly overlooked and not fully assessed. Common assumptions made when modeling geochemical reactions in ASP floods include: 1) ideal solution (i.e., using molalities rather than ion activities) for the water and aqueous geochemical species 2) limiting the number of reactions (i.e., oil/alkali and alkali consumptions) rather than including the entire thermodynamically-equilibrated system 3) ignoring the effect of temperature and pressure on reactions 4) local equilibrium ignoring the kinetics. To the best of our knowledge, the significance of these assumptions has never been discussed in the literature. In this paper we investigate the importance of geochemical reactions during alkaline/surfactant/polymer floods using a comprehensive tool in the sense of surfactant/soap phase behavior as well as geochemistry.
We coupled the United States Geological Survey (USGS) state-of-the-art geochemical tool, with 3D flow and transport chemical flooding module of UTCHEM. This geochemical module includes several thermodynamic databases with various geochemical reactions, such as ion speciation by applying several ion-association aqueous models, mineral, solid-solution, surface-complexation, and ion-exchange reaction. It has capabilities of saturation index calculation, reversible and irreversible reactions, kinetic reaction, mixing solutions, inverse modeling and includes impacts of temperature and pressure on reaction constants and solubility products. The chemical flood simulator has a three phase (water, oil, microemulsion) phase behavior package for the mixture of surfactant/soap, oil, and water as a function of surfactant/soap, salinity, temperature, and co-solvent concentration. Hence, the coupled software package provides a comprehensive tool to assess the significance of geochemical assumptions typically imposed in modeling ASP floods. Moreover, this integrated tool enables modeling of variations in mineralogy present in reservoir rocks. We parallelized the geochemistry module of this coupled simulator for large-scale reservoir simulations.
Our simulation results show that the assumption of ideal solution overestimates ASP oil recovery. Assuming only a subset of reactions for a coupled system is not recommended, particularly when a large number of geochemical species is involved, as is the case in realistic applications of ASP. Reservoir pressure has a negligible effect but temperature has a significant impact on geochemical calculations. Although mineral reaction kinetics is largely a function of the temperature and in-situ water composition, some general conclusions can be drawn as follows: to a good approximation, minerals with slow rate kinetic reaction (e.g., quartz) can be excluded when modeling ASP laboratory floods. However, minerals with fast rate kinetic reactions (e.g., calcite) must be included when modeling lab results. On the other hand, in modeling field-scale applications, local equilibrium assumption (LEA) can be applied for fast rate kinetic minerals, whereas kinetics should be used for slow rate kinetic minerals.
The polymer pilot project performed in the 8 TH reservoir of the Matzen field showed encouraging incremental oil production. To further improve the understanding of recovery effects resulting from polymer injection, an extension of the pilot is planned by adding a second polymer injector.
Forecasting of the incremental oil production needs to take the uncertainty of the geological models and dynamic parameters into account. We propose a workflow which comprises a geological sensitivity and clustering step followed by a dynamic calibration step for decreasing the objective function to improve the reliability of a probabilistic forecast of the incremental oil recovery.
For the geological sensitivity, hundreds of geological realizations were generated taking the uncertainty in the correlation of the sand and shale layers, logs, cores and geological facies into account. The simulated tracer response was used as dissimilarity distance to classify the geological realizations. Clustering was then applied to select 70 representative realizations (centroids) from a total of 800 to use in the full-physics dynamic simulation.
In the dynamic simulation, an objective function comprising liquid rate and tracer concentration of the back-produced fluids was introduced.
To further improve the calibration, the P50 value of incremental oil production as derived from simulation was compared with the incremental oil production determined from Decline Curve Analysis from the wells surrounding the polymer injection well. The mismatch between the P50 and the Decline Curve Analysis was improved by adjusting polymer viscosity.
The calibrated models were then used to for a probabilistic forecast of incremental oil due to an additional polymer injector and to estimate the expected polymer concentration at the producing wells.
The use of isenthalpic flash has become of interest for the simulation of some heavy oil recovery processes where large temperature changes are experienced. For these thermal simulations energy can be used as a primary variable. This leads to thousands or millions of individual multiphase isenthalpic flash calculations. Robust and efficient algorithms for multiple-phase isenthalpic flash are required to improve the efficiency of compositional simulations for thermal recovery.
The general framework on state function based flash specifications proposed by
Narrow boiling mixtures can be dealt with in the majority of cases without any significant difficulty. This is true of the direct substitution algorithm and the proposed solution procedure. The vast majority of examples can be solved without using Q function maximisation. The challenges associated with multiphase calculations in the Newton steps are investigated. In particular, inadequate initial estimate of the equilibrium type may lead to non-convergent iteration. This can usually be solved by introduction of a new phase and/or elimination of an existing phase. The speed of the method is analysed for a large number of specifications and is found to be only slightly more expensive than isothermal flash in the majority of cases.
Hou, Binchi (Research Institute of Shaanxi Yanchang Petroleum (Group) CO., LTD.) | Liu, Hongliang (China Petroleum Logging TuHa Business Division) | Bian, Huiyuan (Xi'an University of Science and Technology) | Wang, Chengrong (China Petroleum Logging TuHa Business Division) | Xie, Ronghua (Daqing Oilfield CO.LTD., PetroChina) | Li, Kewen (China University of Geosciences(Beijing)/Stanford University)
Capillary pressure and resistivity in porous rocks are both functions of wetting phase saturation. Theoretically, there should be a relationship between the two parameters. However, few studies have been made regarding this issue. Capillary pressure may be neglected in high permeability reservoirs but not in low permeability reservoirs. It is more difficult to measure capillary pressure than resistivity. It would be useful to infer capillary pressure from resistivity well logging data if a reliable relationship between capillary pressure and resistivity can be found. To confirm the previous study of a power law correlation between capillary pressure and resistivity index and develop a mathematical model with a better accuracy, a series of experiments for simultaneously measuring gas-water capillary pressure and resistivity data at a room temperature in 16 core samples from 2 wells in an oil reservoir were conducted. The permeability of the core samples ranged from 9 to 974 md. The gas-water capillary pressure data were measured with confining pressures using a semi-porous plate technique. We developed the specific experimental apparatus to measure gas-water capillary pressure and resistivity simultaneously. The results demonstrated that the previous power law model correlating capillary pressure and resistivity works well in many cases studied. A more general relationship between the exponent of the power law model and the rock permeability was developed and verified using the experimental data.
In this paper, we estimate foam parameters and investigate foam behavior for a given range of water saturation using two local equilibrium foam models: the population balance and the Pc*. Our method uses an optimization algorithm to estimate foam model parameters by matching foam measured pressure gradient from steady-state coreflood experiments. We calculate the effective foam viscosity and the water fractional flow using experimental data and we then compare lab data against results obtained with the matched foam models to verify the foam parameters. Other variables, such as the foam texture and foam relative permeability are used to further investigate the behavior of the foam during each experiment. We propose an improvement to the Pc* model with a better match in high quality regime by assuming resistance factor and critical water saturation is a linear function of pressure gradient. Results show that the parameter estimation method coupled with an optimization algorithm successfully matches the experimental data using both foam models. In the population balance, we observe different values of the foam effective viscosity for each pressure gradient due to variations of the foam texture and shear thinning viscosity effect. The Pc* model presents a constant effective viscosity for each pressure gradient; we propose the use of resistance factor and critical water saturation as a linear function of pressure to improve the match in the high quality regime, when applicable. Foam has been successfully used in the oil industry for conformance and mobility control in gas injection processes. The efficiency of a foam injection project must be assessed by means of numerical models. Although there are several foam flow models in the literature, the prediction of foam behavior is an important issue that needs further investigation.
As polymer injection has not reached the same maturity as waterflooding, implementing polymer injection projects at field scale requires a workflow comprising screening of the portfolio of an organization for oil fields potentially amenable for polymer injection, laboratory and field testing followed by sector- and field implementation and roll-out in the portfolio.
Going through the workflow, not only the subsurface uncertainty is reduced but also the knowledge about the cost structure and operating capabilities of the organization improved.
Analyzing the economics of polymer injection projects shows that costs can be split into polymer injector-producer (polymer pattern) dependent and independent costs. Knowing these costs, a Minimum Economic Number of Patterns (MENP) is defined to achieve Net Present Value zero. This number is used to determine a Minimum Economic Field Size (MEFS) for polymer injection which is taken into account in the screening of the portfolio.
Defining a robustness criterion for economics, the minimum number of patterns for polymer injection meeting this criterion is calculated. This criterion is applied to generate a diagram allowing for screening of fields for polymer economics using pattern dependent and pattern independent costs and Utility Factor.
The cost structure reveals how the NPV of polymer projects changes with number of patterns, incremental oil and injectivity. Injectivity is of particular importance as it determines the Chemical Affected Reservoir Volume (CARV) or speed of production.
A sensitivity analysis of the NPV showed that for the cost structure used here, in addition to the polymer costs, the well costs are important for the economics of a full-field polymer injection project.
Given limited CO2 supply, operational constraints, and pattern specific reservoir performance, WAG schedule can be customized such that NPV or other metrics are optimized. Depending on the WAG schedule, recovery can fluctuate between 5–15% at the pattern scale due to reservoir heterogeneity causing variations in sweep efficiency. An analytical method was developed to optimize WAG schedules that couples traditional reservoir modeling and simulation with machine learning, enabling the discovery of optimal WAG schedules that increase recovery at the pattern level. A history-matched reservoir model of Chaparral Energy's Farnsworth Field, Ochiltree County, TX was sampled intelligently to perform predictive reservoir flow simulations and artificially build an intelligent reservoir model that samples a broad range of possible WAG scenarios for optimization. The intelligent model generates the next "best" sample to investigate in the numerical simulator and converges on the optima, quickly reducing the number of runs investigated. Results in this paper demonstrate that there can be significant improvements in net present value as well as net utilization rates of CO2 using this analytical technique. The WAG design generated by the intelligent reservoir model should be deployed in the field in early 2016 for validation. It is intended that the intelligent reservoir model will be updated on a regular basis as injection and production data is obtained. This effort represents the beginning of a paradigm shift in the application of modeling and simulation tools for significant improvements in field production operations.
Pilots are widely used for the purpose of gathering valuable information about performance and practical challenges of implementing a particular CEOR process in a given field (
Addition of chemical species to the material balance equations alongside finer resolution requirements for CEOR simulations compared to waterfloods (WF), often make it impractical to run full field CEOR simulations to the required accuracy. Massively parallel computing, dynamic local grid refinement and sector modeling have been used with varying success, of which sector modeling is the most common. Sector models, by their very definition, are also naturally suited for modeling of pilots.
The art of sector modeling needs mastering a few important steps such as: appropriate selection of the sector model extent, details on carving it out of the Full Field Model (FFM), populating it with proper petrophysical and fluid properties, initializing it to correct initial conditions and optimizing its boundary conditions. On top of that, choice of optimum grid size for proper trade-off of simulation run times and accuracy needs to be considered.
This paper presents a case study for appropriate simulation of a CEOR pilot within Chevron. The candidate has a waterflood history matched FFM. This model is used to generate a sector model for the CEOR pilot area. This paper outlines how the extent of the sector model and all the regions in communication with the Area of Interest (AOI) is decided. It also discusses proper initialization and optimization of the boundary conditions of the sector model along with its appropriate refinement and grid optimization. Proper CEOR simulations on the final optimized sector model and sensitivity analysis are also presented. The challenges, lessons learned and best practices are shared and important considerations for adequate simulation of CEOR processes are outlined.
Aldhaheri, Munqith N. (Missan Oil Company, Missouri University of Science and Technology) | Wei, Mingzhen (Missouri University of Science and Technology) | Bai, Baojun (Missouri University of Science and Technology)
Polymer gels are increasingly applied to improve sweep efficiency of different IOR/EOR recovery processes. Three in-situ polymer gel systems including bulk gels, colloidal dispersion gels, and weak gels are often used to mitigate water production caused by reservoir heterogeneity and unfavorable mobility ratio of oil and injected fluids. Selecting the most appropriate gel system is a key component for a successful conformance improvement treatment. Screening criteria in terms of reservoir and fluid characteristics have been widely used to identify potential technologies for a specific reservoir. Despite the large number of polymer gel projects, only five, limited-parameters, single-agent criteria or surveys have been sporadically accomplished that suffer from many deficiencies and drawbacks.
This paper presents the first complete applicability guidelines for gel technologies based on their field implementations in injection wells from 1978 to 2015. The data set includes 111 cases histories compiled mainly from SPE papers and U.S. Department of Energy reports. We extracted missing data from some public EOR databases and detected potential outliers by two approaches to ensure data quality. Finally, for each parameter, we evaluated project and treatment frequency distributions and applicability ranges based on successful projects. Extensive comparisons of the developed applicability criteria with the previous surveillance studies are provided and differences are discussed in details as well.
In addition to the parameters that are considered for other EOR technologies, we identified that the applicability evaluations of polymer gels should incorporate the parameters that depict roots and characteristics of conformance issues. The present applicability criteria comprise 16 quantitative parameters including permeability variation, mobility ratio, and three production-related aspects. Application guidelines were established for organically crosslinked bulk gels for the first time, and many experts' opinions in the previous criteria were replaced by detailed property evaluations. In addition, we identified that the applicability criteria of some parameters are considerably influenced by lithology and formation types, and thus, their data were analyzed according to these characteristics. Besides their comprehensiveness of all necessary screening parameters, the novelty of the new criteria lies in their ability to self-check the established validity limits for the screening parameters which resulted from the inclusion and simultaneous evaluation of the project and treatment frequencies.