Yang, Zhaopeng (PetroChina Research Institute of Petroleum Exploration&Development) | Li, Xingmin (PetroChina Research Institute of Petroleum Exploration&Development) | Chen, Heping (PetroChina Research Institute of Petroleum Exploration&Development) | Ramachandran, Hariharan (The University of Texas at Austin, Hildebrand Department of Petroleum and Geosystems Engineering) | Shen, Yang (PetroChina Research Institute of Petroleum Exploration&Development) | Yang, Heng (China National Oil and Gas Exploration and Development Corporation) | Shen, Zhijun (China National Oil and Gas Exploration and Development Corporation) | Nong, Gong (China National Oil and Gas Exploration and Development Corporation)
The block M as a foamy extra-heavy oil field in the Carabobo Area, the eastern Orinoco Belt, has been exploited by foamy oil cold production utilizing horizontal wells. The early producing area has been put into production about 10 years, existing problems of productivity declining and produced gas-oil ratio rising. Therefore, the development optimization for the early producing area should be conducted in order to obtain the more profitable oil recovery. A typical foamy oil reservoir simulation model using 5 components was created to understand the remaining oil distribution features. Based on above understandings, technical strategies were proposed for infilling well deployment in the early producing area. Results show that the gravity drainage and gravity differentiation of oil and gas during the cold production of foamy extra-heavy oil from horizontal wells by foam flooding are the main mechanisms for formation of remaining oil. And the influence factors of remaining oil distribution include horizontal well spacing, reservoir thickness, reservoir heterogeneity, interlayer distribution and reservoir rhythm. Thus tor foamy extra-heavy oil CHOP process, the enriched remaining oil area is the place between two adjacent horizontal wells with well spacing of 600m. Therefore, well infilling is an effective measure improving oil recovery factor of cold production, and the well infilling should be implemented as soon as possible to obtain better performance of cold production.
This paper submits the monitoring methodology applied to horizontal wells associated to the First polymerized water injection pilot project, developed in Zuata Principal Field from Hugo Chavez Orinoco Oil Belt (Venezuela). Zuata Principal is a mature field, formed by unconsolidated sands of deltaic and fluvial sedimentary environments, saturated with extra heavy crude of API gravity between 8.5 to 9.5, and viscosities between 2000 to 5000 cp at reservoir conditions. The basic units of production construction (Clusters) are mostly made of horizontal wells perforated in a radial pattern, which operate under the artificial lifting method of progressive cavity pump (PCP). The pilot project was developed in a deltaic environment. As a part of the surveillance plan of the project, it was established one methodology for the control of the producer wells, using the following sources of information: - Measurements of pressure and temperature at bottom hole (real time), using multiple pressure and temperature sensors placed in the horizontal section and temperature distributed sensors, meaning fiberoptic sensors.
Schedule Session Details Expand All Collapse All Filter By Date All Dates Sunday, December 09 Monday, December 10 Tuesday, December 11 Wednesday, December 12 Filter By Session Type All Sessions General Activities Social and Networking Events Technical Sessions Panel, Plenary, and Special Sessions Energy4Me Training Course/Seminar Sunday, December 09 07:00 - 15:00 Field Trip: An Integrated Approach to Geologic Outcrops for Boosting Reservoir Understanding Jal Az Zor Escarpment, North of Kuwait City Ticketed Event Field Trip Jal Az Zor Integrated Field Course An Integrated Approach to Geologic Outcrops for boosting Reservoir Understanding When: 9 December 2018 Where: Jal Az Zor Escarpment, north of Kuwait City Organizers: KOC, with KOC and Shell SMEs The field trip will provide an integrated approach to geologic outcrops, using Jal Az Zor examples, that will trigger reflections in the participants about the implications of heterogeneities, scale, and 3D distribution of rock properties to models, studies, activities, and insights pertinent to reservoir analysis. The field course is specifically designed to relate the geology to a variety of subsurface disciplines involved in heavy oil development. Topics addressed will include baffles, reservoir modelling, steam conformance, cap rock integrity, well spacing, integration of well, reservoir, and facilities management (WRFM), and observation wells placement. The ultimate goals are to gain an appreciation for the value that the understanding of vital elements of rock description and sedimentology have for reservoir studies, and for the enhancement of production strategies. Group discussion will be encouraged to share knowledge and trigger new perspectives.
The objective of the study was to estimate how much the mobility of a polymeric solution is affected at reservoir conditions, in an enhanced oil recovery process using polymer. This document describes the different techniques and methodologies to establish polymer solution degradation, and its effects over the expected behavior.
The analysis was performed using the results from 4 fall off tests at different stages of the injection process, the test were executed every three months after the beginning of the injection of the polymer solution, following the surveillance plan established. Other diagnostics techniques were also studied, in order to discard geologic features that could affect the injection process, among then: Hall plot diagnostics and temperature logging with fiber optics sensors.
The mobility of the polymer solution at reservoir conditions was determined. The affectation of the polymer solution is related to particular conditions of each section of the reservoir, meaning that minerals in the reservoir rock, and salinity of the connate water, could be the possible reasons why the polymer was affected, and exhibited a higher mobility compared to the design parameters. Later it was observed that the polymer mobility decreased over time, indicating that the polymer solution was no longer affected by in situ conditions.
To establish the performance of an enhanced recovery process using polymer, in the case of extra heavy oil reservoirs, it is necessary to evaluate the actual performance, and depend not only of the core test and simulation results. The analysis accomplished in this work was used to obtain important information necessary to asset feasibility, in the case of a larger scale implementation.
This paper submits the monitoring methodology applied to horizontal wells associated to the First polymerized water injection pilot project, developed in Zuata Principal Field from Hugo Chavez Orinoco Oil Belt (Venezuela). Zuata Principal is a mature field of unconsolidated sands of deltaic and fluvial sedimentary environments, saturated with extra heavy crude of API gravity between 8.5 to 9.5, and viscosities between 2000 to 5000 cp at reservoir conditions. The basic units of production construction (Clusters) are mostly made of horizontal wells perforated in a radial pattern, which operate under the artificial lifting method of progressive cavity pump (PCP). The pilot project was developed in a deltaic environment.
Advanced well structures have continued to see an increase in field applications, particularly in unconventional reservoir development. Traditional methods for well performance analyses barely capture the complex interactions between these well structures and the reservoir. In this study, a set of artificial expert systems has been developed to predict the performance of advanced well structures in tight oil reservoirs given a set of reservoir properties.
The artificial expert systems are built on a system of neural networks. Data for training and validation was generated by building feature sets that contain inputs that were randomly selected within a broad range of limits to capture properties of a typical tight oil reservoir. The features set for inputs into the neural networks includes reservoir extent data, rock and fluid properties, relative permeability and capillary pressure data, as well as well design and operating condition data. Each feature set is run through a commercial numerical simulator to provide time-series data of cumulative oil, gas, water production, as well as bottom-hole pressure profile where applicable. Both the feature set and the time- series data are used for training the neural networks.
Generalization tests conducted on each expert system using a validation dataset show that the outputs of the expert systems compare closely with results from the numerical simulator for the same dataset with an average Mean Absolute Error
This study shows that an artificial expert system, once trained properly, provides a very practical, fast and robust method for predicting production performance in tight oil reservoirs using advanced well structures. This helps to eliminate the snag of developing an in-depth mathematical model to describe the complex relationships between variables involved, and also increases efficiency in well planning by providing quick, accurate estimates for production forecasts, thus reducing the iteration time spent between the reservoir engineer and other disciplines.
In this study we performed a seismic interpretation from reflected PP and PS converted wave data acquired over 95 Km2 in the Orinoco oil belt, Venezuela. The goal of this study was to incorporate the PS wave information to improve a heavy oil reservoir characterization and to enhance the reservoir delineation in a complex stratigraphic setting. In order to achieve the scope several sources of information were analyzed and integrated. Compressional and shear wave slowness logs where used to estimate the PS time-depth function after scaling the PP time-depth functions available in the location covered by the seismic survey. The PS time-depth functions obtained were used to transform well logs and tops from depth to PS reflection time. We generated PP synthetic seismogram in PS time domain to calibrate the PS seismic volume and proceed to interpret the PS seismic volume.
After calibration of the PP and PS seismic volumes two horizons were interpreted along the reflections associated with the target reservoir. These horizons were used to estimate seismic attributes in both PP and PS seismic volumes. We found that the attribute amplitude weighted by instantaneous frequency of the PS-wave correlated better with sand thickness than the same attribute estimated from PP-wave. This can be partly attributed to the differences in resolution between PP and PS-wave. We conclude that the low resolution of the PS-wave make it more sensitive to thickness variation of the sand bodies at the target level due to the tuning effect.
Presentation Date: Tuesday, September 26, 2017
Start Time: 1:50 PM
Presentation Type: ORAL
Rodriguez, Ricardo (PDVSA) | Villavivencio, Elvio (PDVSA) | Bellorin, Pavel (PDVSA) | Rendon, Lerrys (PDVSA) | Orozco, Jose (Schlumberger) | Quintero, Andreina (Schlumberger) | Chapellin, Alvaro (Schlumberger) | Mutina, Albina (Schlumberger) | Bammi, Sachin (Schlumberger)
The Orinoco Oil Belt (Faja) is the largest known heavy oil reserve in the planet. Geologically, its reservoirs are composed mainly of sequences of shales and unconsolidated sands. The properties of the sand units such as shale volume, water saturation, porosity, and thickness can present lateral heterogeneity at a few hundred feet scale. The high viscosity of the oil and its variation both laterally and vertically is one of the key features of the Faja. Prediction of water saturation from resistivity can be difficult due to multiple reasons, including the low salinity of the formation water and wettability changes.
For the field development, Faja reservoirs are drilled following a specific drilling pattern called a “macolla”. A macolla is composed of a vertical stratigraphic well followed by a group of two to four highly deviated wells (slant wells). These deviated wells play a fundamental role in cluster delineation, because they are key calibration points in the trajectory planning of the subsequent set of horizontal wells, which are completed with a slotted liner to maximize production.
Usually, in Faja, only vertical stratigraphic wells include comprehensive logging suites. These suites include elemental gamma ray spectroscopy, microresistivity images, sonic, dielectric, and magnetic resonance measurements at multiple depths of investigation. Moreover, due to the complexity of logging highly deviated wells in unconsolidated formations, many slant wells are not logged or logged only for correlation (gamma ray and resistivity logs). The ability to acquire more log data in the slant wells improves reservoir description and reduces the uncertainty in the planning of horizontal production wells.
The case study presented here illustrates the value of integrating data from vertical and slant wells in a macolla cluster. Comprehensive logging suites acquired in the vertical wells are complemented with through-the-bit logging suites acquired in the slant wells. Through-the-bit technology has recently been introduced in Venezuela and has proved to enable the acquisition of high quality logs through unconsolidated sand shale sequences in highly deviated boreholes. Rig time due to the logging operation and the risk of sticking of the logging string was also reduced.
This case study presents the workflow for and the results of the multiwell data integration in which different formation properties, including lithology-based facies, are propagated and incorporated into a 3D structural model. This workflow provides critical input to reservoir characterization and facilitates significantly the planning of horizontal wells.
Huyapari is a giant field, located in the Orinoco Heavy Oil Belt of eastern Venezuela. Huyapari contains huge original oil in place (OOIP) of extra heavy crude oil (7 to 9°API) with excellent reservoir properties that enable primary production of the extra heavy crude oil by using long horizontal wells. Nevertheless, the live oil viscosity variation at reservoir conditions (1,500 to 20,000 cp) represents a production challenge in the field. This study aims to improve the fluid heterogeneity understanding in the field through the application of PVT (Pressure, Volume and Temperature) and geochemical analysis for oil viscosity estimation.
Fluid heterogeneity mapping using crude fingerprint analyses was performed to understand the variability of the oil biodegradation level across the field. PVT data provided reservoir GOR and supported the oil chemical variation. Biomarkers correlations were also evaluated to obtain a better estimation of oil viscosity, and then compared with oil viscosity measurements performed at surface and reservoir conditions.
The integration of geochemical analysis with the PVT data allowed to improve the Huyapari field correlations used for well potential estimation. A shallow reservoir of the field with few production wells and larger prospective areas was chosen to evaluate its oil viscosity variation and the methodology application. A better well placement and reservoir management strategy was established, thus demonstrating the value of this data integration.
This study demonstrates that reservoir geochemistry coupled with reservoir engineering data is a cost-effective reservoir management tool. This methodology could be useful for application in other extra-heavy oil fields where little reservoir geochemistry input has been considered in their field simulation procedures.
The principal objective of this paper is to develop an artificial expert system capable of instantaneously and accurately predicting complex wells (CW) performance, proposing CW designs, and predicting average reservoir properties, for shale gas wells operating under specified bottom-hole pressure. Artificial neural networks (ANN) provide the backbone of the expert system. Other methods, such as traditional well-testing, numerical reservoir simulation, and decline curve analysis, have inherent limitations or require significant time and effort expended. ANN methodology has the ability to recognize patterns between various parameters in the presence of large databases and is a powerful tool especially when the existing relationships between the dependent and independent parameters are vague or are not well understood. Thus, it is capable of instantly solving problems that do not have known analytical or numerical solutions. CW are scarce in shale gas reservoirs, thus utilizing real data in ANN training is not possible most of the time. Accordingly, numerical reservoir simulation is used to generate the database necessary for training the expert system. The expert system developed in this research instantly and accurately performs the tasks below for shale gas CW operating under constant bottom-hole pressure conditions and they are capable of: 1. Predicting production rates for a given CW design from a given shale gas reservoir.