An application of stratal slice technique in channel sand body reservoir description in dense well pattern zone–taking North III West area of Saertu oil field as an example

2016 ◽  
Author(s):  
Zhou Huajian* ◽  
Qi Jincheng ◽  
Yang Wenmin ◽  
Li Hongxing ◽  
Jiao Yanli
Keyword(s):  
Oil Field ◽  
Sand Body ◽  
Well Pattern ◽  
Stratal Slice ◽  
Reservoir Description

Reservoir prediction in a development area with a high-density well pattern using seismic sedimentology: An example from the BB2 block, Changyuan LMD oil field, Songliao Basin, China

2015 ◽  
Vol 3 (3) ◽  
pp. SS87-SS99 ◽  
Author(s):  
Shunguo Cheng ◽  
Yan Jiang ◽  
Jie Li ◽  
Cao Li ◽  
Yanhui Wang ◽  
...  
Keyword(s):  
Average Density ◽  
Oil Field ◽  
Reservoir Prediction ◽  
Log Data ◽  
Field Development ◽  
Well Pattern ◽  
Infill Drilling ◽  
Seismic Sedimentology ◽  
Reservoir Description ◽  
Sand Bodies

The Daqing Changyuan oil field is primarily composed of large, fluvial-deltaic thin sandstones and shales with a high degree of heterogeneity. Over the past 50 years of development, the geologic study of this reservoir has relied on a large amount of well-log data in the field. However, a detailed reservoir description based only on wireline-log data cannot meet the requirements of oil field development. There is still some uncertainty about the sand boundary and geometry, due to reliance only on data from fields with an average density of approximately [Formula: see text]. Such uncertainty may severely affect the potential for producing the remaining oil in these mature oil fields. In this study, seismic-sedimentology guided reservoir prediction is examined in an area of dense wells in BB2 block in the Changyuan LMD oil field. The spatial distribution of channel-sand bodies was identified and recognized by facies analysis, sandstone thickness mapping, and seismic stratal slicing of reservoir units, using the principles and methods of seismic sedimentology. The results showed that the seismic amplitude can be correlated to log lithologies. The interpretation of sandstone can be improved by 90°-phase seismic data, and the distribution of channel sand with a thickness greater than 5 m can be directly predicted. The identification and prediction of the boundaries of channel-sand bodies are thus improved. The results have proved useful in new infill drilling and reperforations.

Integrated Solutions for the Redevelopment Plan of Offshore Mature-Oilfield

2021 ◽  
Author(s):  
Hongfu Shi ◽  
Yingxian Liu ◽  
Lifu Jiang ◽  
Jingding Zheng ◽  
Liqin Gan
Keyword(s):  
High Water ◽  
Bohai Bay ◽  
Sand Body ◽  
Well Pattern ◽  
Open Hole ◽  
Average Water ◽  
High Water Cut ◽  
Water Cut ◽  
Stream Lines ◽  
Sand Bodies

Abstract Abundant faults, long oil-bearing intervals (up to 500m), and diverse fluids including conventional oil and heavy oil, result in P oilfield became one of the most complex oil fields in the Bohai Bay. The main characters ofinitial development plan are directional well with commingle production, open hole completion, large draw down, high oil production rate, and reverse nine-point well pattern. At present, the oilfield has entered a stage of high water cut, with average water cut more than 85%. What can we do next, decommissioning or rebirthing? An integrated solution was proposed to redevelop the oilfield which focus on the layers’ subdivision, the fine description of the sand body,a large number of horizontal wells on the top of the water-flooded layer are used to tap the potential, increase the water injector to transform the stream lines and rebuild the reservoir pressure, and search for potential sand bodies to increase reserves.

Artificial Intelligence Method of Flow Unit Division Based on Waveform Clustering: A Case Study on Zhetybay Oil Field, South Mangyshalk Basin, Kazakhstan

2021 ◽  
Author(s):  
Libing Fu ◽  
Jun Ni ◽  
Yuming Liu ◽  
Xuanran Li ◽  
Anzhu Xu
Keyword(s):  
Artificial Intelligence ◽  
Oil Field ◽  
Flow Unit ◽  
Beach Sand ◽  
Flow Index ◽  
Support Vector ◽  
Main Body ◽  
Flow Units ◽  
Well Pattern ◽  
Channel Combination

Abstract The Zhetybay Field is located in the South Mangyshlak Sub-basin, a delta front sedimentary reservoir onshore western Kazakhstan. It was discovered in 1961 and first produced by waterflooding in 1967. After more than 50 years of waterflooding development, the reservoirs are generally in the mid-to-high waterflooded stage and oil-water distribution becomes complicated and chaotic. It is very difficult to handle and identify so much logging data by hand since the oilfield has the characteristics of high-density well pattern and contains rich logging information with more than 2000 wells. The wave clustering method is used to divide the sedimentary rhythm of the logging curve. Sedimentary microfacies manifested as a regression sequence, with four types of composite sand bodies including the composite estuary bar and distributary channel combination, the estuary bar connected to the dam edge and the distributing channel combination, the isolated estuary bar and distributing channel combination, and the isolated beach sand. In order to distinguish the flow units, the artificial intelligence algorithm-support vector machine (SVM) method is established by learning the non-linear relationship between flow unit categories and parameters based on developing flow index and reservoir quality factor, summarizing permeability logarithm and porosity degree parameters in the sedimentary facies, and analyzing the production dynamic. The flow units in Zhetybay oilfield were classified into 4 types: A, B1, B2 and B3, and the latter three are the main types. Type A is distributed in the river, type B1 is distributed in the main body of the dam, type B2 is mainly distributed in the main body of the dam, and some of B2 is distributed in the dam edge, and B3 is located in the dam edge, sheet sand and beach sand. The results show that the accuracy of flow unit division by support vector machines reaches 91.1%, which clarifies the distribution law of flow units for oilfield development. This study is one of the significant keys for locating new wells and optimizing the workovers to increase recoverable reserves. It provides an effective guidance for efficient waterflooding in this oilfield.

Steam Drive Small "Back" Glyph Pattern Injection-Production Parameters Optimization Research

2013 ◽  
Vol 868 ◽  
pp. 629-632
Author(s):  
Gui Man Liu
Keyword(s):  
History Matching ◽  
Field Work ◽  
Simulation Software ◽  
Oil Field ◽  
Parameters Optimization ◽  
Production Parameters ◽  
Theoretical Support ◽  
Steam Drive ◽  
Well Pattern ◽  
Injection Ratio

Jin 45 block of Liaohe oil field work with inverted nine-spot which has a low production-injection ratio of 3:1, but now it has a phenomenon of energy spillover. On the basis of that, design a small "back" glyph injection-production well pattern, this pattern has a higher production-injection ratio of 9:1. Petrel software makes geologic model, through the CMG numerical simulation software for steam huff and puff history matching, on the small back glyph pattern injection-production parameters optimization makes the 7.5 years of steam drive prediction. The results of the study show that: the little back glyph pattern in variable speed gas injection has the advantage of the high gas oil ratio and low production speed , more suitable for heavy oil steam drive development. It provides theoretical support for the steam drive of Jin 45 block of Liaohe oil field's expanding.

A Preliminary Study on Sedimentary Mode of Putaohua Oil Layer in the East of Sanzhao Depression

2012 ◽  
Vol 524-527 ◽  
pp. 3-9
Author(s):  
Lin Cong ◽  
Shi Zhong Ma ◽  
Yu Sun ◽  
Ru Bin Li
Keyword(s):  
Transition Region ◽  
Developmental Process ◽  
Sedimentary Facies ◽  
Oil Field ◽  
Sedimentary Characteristics ◽  
Sand Surface ◽  
Sand Body ◽  
Delta System ◽  
Preliminary Study ◽  
Distributary Channel

Based on ten well cores, seventeen hundred logging data and initial potential data, sedimentary characteristics and mode of shallow lacustrine fluvial-dominated delta of Putaohua oil layer in the east of Sanzhao depression were analyzed. It is realized that distributary channel sandbodies as sand body framework of this delta system, which is abundant, closely and narrow, and the framework of sand body is in a large number of narrow banded shape (mostly 200 ~ 300m), and can extend hundreds of kilometers of continuous, dense, overall was SW, and combines well with other types sand surface to become a better distributary channel sand body. Based on understanding of sedimentary background, developmental process, sedimentary characteristics and sedimentary facies type of Putaohua oil layer in Sanzhao depression, sedimentary mode of shallow lacustrine fluvial-dominated delta is established in the study area, and sedimentary mode of five subfacies is further divided: Delta distributary plain subfacies is mode of fluvial-dominated belt body; Transition region of front-distributary plain is mode of inshore; Inner front is mode of fluvial-dominated belt body; Transition region of inner front-outer front is mode of fluvial-dominated sheet sand; Shallow lacustrine fluvial-dominated delta outer front is mode of tide-dominated sheet sand; Also pointed out that overall shows NE –SW trending submerged distributary channel sandbodies which is abundant, closely and narrow is the main reservoir of the study area. It provides the solid geological basis for the establishment of spatial distribution pattern of reservoir; identify the causes of mainly monosandbody and further tapping the potential of oil field.

Research on Efficient Microbial Enhanced Oil Recovery Technology Based on Low-Temperature Heavy Oil

2013 ◽  
Vol 734-737 ◽  
pp. 1434-1439 ◽  
Author(s):  
Gang Wu ◽  
Fu Ping Ren ◽  
Jing You ◽  
Ji Liang Yu ◽  
Ya Tuo Pei ◽  
...  
Keyword(s):  
Low Temperature ◽  
Enhanced Oil Recovery ◽  
Oil Recovery ◽  
Heavy Oil ◽  
Average Concentration ◽  
Oil Field ◽  
Oil Reservoir ◽  
Oil Degradation ◽  
Microbial Enhanced Oil Recovery ◽  
Well Pattern

Based on the low-temperature and heavy oil reservoir of conventional injection well pattern separated two strains of oil degradation bacteria LC and JH which had satisfactory compatibleness with BaoLige oill field. In order to study the feasibility of enhancing oil recovery rate of the two strains, the experiment of huff and puff with 15 wells were carried out. The average concentration of bacteria increase from 4.7×102cells/ml to 8.1×106cells/ml. The average reduction of surface tension and viscosity is 33.1% and 31.9%. The accumulative total was 1163.2t. The ratio of input to output was 1:2.12. Microbial enhanced oil recovery can improve the low-temperature and heavy oil production status, which provide a effective method for the similar oil field.

Steamflood With Vertical Injectors and Horizontal Producers in Multiple Zones

2011 ◽  
Vol 14 (02) ◽  
pp. 161-170 ◽  
Author(s):  
Sanjeev Malik ◽  
Y.M.. M. Zhang ◽  
Mohammed Al Asimi ◽  
Thomas L. Gould
Keyword(s):  
Oil Recovery ◽  
Thermal Model ◽  
Steam Injection ◽  
Oil Field ◽  
Sultanate Of Oman ◽  
Vertical Wells ◽  
Final Design ◽  
Reservoir Description ◽  
Thermal Simulations ◽  
Enhance Oil Recovery

Summary The Mukhaizna heavy-oil field in the Sultanate of Oman desert has three distinct zones that require steam injection to enhance oil recovery. A new, geocellular-based reservoir description was prepared to evaluate the steamflood performance of these three zones using different horizontal- and vertical-well configurations. On the basis of the results of thermal simulations, the final design called for vertical wells injecting steam into all three zones, with three stacked horizontal production (HP) wells, one for each zone. One advantage of this design is the ability to control the steam flux from each vertical injector (VI) into each zone to mitigate early steam breakthrough and optimize recovery. After 2 years of steam injection, oil production is tracking the thermal model nicely.

Integrated Automation and Data-Driven Workflow for CO2 Project Management – Case Study from a Smart Oil Field in the Middle-East

2021 ◽  
Author(s):  
Erismar Rubio ◽  
Mohamed Yousef Alklih ◽  
Nagaraju Reddicharla ◽  
Abobaker Albelazi ◽  
Melike Dilsiz ◽  
...  
Keyword(s):  
Project Management ◽  
Real Time ◽  
Reservoir Management ◽  
Oil Field ◽  
Data Driven ◽  
Co2 Injection ◽  
Well Performance ◽  
Potential Cost ◽  
Integrated Automation ◽  
Well Pattern

Abstract Automation and data-driven models have been proven to yield commercial success in several oil fields worldwide with reported technical advantages related to improved reservoir management. This paper demonstrates the implementation of an integrated workflow to enhance CO2 injection project performance in a giant onshore smart oil field in Abu Dhabi. Since commissioning, proactive evaluation of the reservoir management strategy is enabled via smart-exception-based surveillance routines that facilitate reservoir/pattern/well performance review and supporting the decision making process. Prolonging the production sustainability of each well is a key pillar of this work, which has been made more quantifiable using live-tracking of the produced CO2 content and corrosion indicators. The intensive computing technical tasks and data aggregation from different sources; such as well testing and real time production/injection measurements; are integrated in an automatic workflow in a single platform. Accordingly, real-time visualizations and dashboards are also generated automatically; to orchestrate information, models and multidisciplinary knowledge in a systematic and efficient manner; allowing engineers to focus on problematic wells and giving attention to opportunity generation in a timely manner. Complemented with numerical techniques and other decision support tools, the intelligent system data-driven model assist to obtain a reliable short-term forecast in a shorter time and help making quick decisions on day-to-day operational optimization aspects. These dashboards have allowed measuring the true well/pattern performance towards operational objectives and production targets. A complete set of KPI's has helped to identify well health-status, potential risks and thus mitigate them for short/long term recovery to obtain an optimum reservoir energy balance in daily bases. In case of unexpected well performance behaviors, the dashboards have provided data insights on the root causes of different well issues and thus remedial actions were proposed accordingly. Maintaining CO2 miscibility is also ensured by having the right pressure support around producers, taking proactive actions from continues evaluation of producer-injector connectivity/interdependency, improving injection/production schedule, validating/tuning streamline model based on surveillance insights, avoiding CO2 recycling, optimizing data acquisition plan with potential cost saving while taking preventive measures to minimize well/facility corrosion impact. In this work, best reservoir management practices have been implemented to create a value of 12% incremental oil recovery from the field. The applied methodology uses an integrated automation and data-driven modeling approach to tackle CO2 injection project management challenges in real-time.

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