Oil Water Layers Research Articles

A new method for fluid identification and saturation calculation of low contrast tight sandstone reservoir

The resistivity difference between oil and gas layers and the water layers in low contrast tight sandstone reservoirs is subtle. Fluid identification and saturation calculation based on conventional logging methods are facing challenges in such reservoirs. In this paper, a new method is proposed for fluid identification and saturation calculation in low contrast tight sandstone reservoirs. First, a model for calculating apparent formation water resistivity is constructed, which takes into account the influence of shale on the resistivity calculation and avoids apparent formation water resistivity abnormal values. Based on the distribution of the apparent formation water resistivity obtained by the new model, the water spectrum is determined for fluid identification in low contrast tight sandstone reservoirs. Following this, according to the average, standard deviation, and endpoints of the water spectrum, a new four-parameter model for calculating reservoir oil and gas saturation is built. The methods proposed in this paper are applied to the low contrast tight sandstone reservoirs in the Q4 formation of the X53 block and X70 block in the south of Songliao Basin, China. The results show that the water spectrum method can effectively distinguish oil-water layers and water layers in the study area. The standard deviation of the water spectrum in the oil-water layer is generally greater than that in the water layer. The new four-parameter model yields more accurate oil and gas saturation. These findings verify the effectiveness of the proposed methods.

Sedimentological and diagenetic effects on petrophysical characteristics and hydraulic flow units of Zhujiang sandstones in the Pearl River Mouth Basin, south China Sea

The Zhu-Ⅲ sag is a key oil and gas rich basin. The Zhujiang Formation is characterized by strong heterogeneity, a changeable sedimentary environment and complex diagenesis, which presents great challenges in the identification of the reservoir sweet spot. Previous researchers have almost no study on it. Therefore, finding high-quality reservoir units is the top priority. In this study, we focus on classifying and evaluating the reservoir units, finding high-quality units and providing exploration recommendations. We carried out core observations and description, X-ray diffraction, cast thin-section observations, electron microscopy observations and mercury intrusion experiments. We used the normal probability distribution model to divide the hydraulic flow unit into six types and determined their hydraulic flow capacity. We summarized the depositional model of the study area through core observations and well analysis. By combining the data on the hydraulic flow units, pore throat radius, sedimentary background, diagenesis and reservoir physical properties, we classified six rock facies (RFs). We found that there is good correspondence between rock facies, hydraulic flow units and sedimentary environments. The results indicate that the quality of the RF-1, RF-2 and RF-3 reservoirs are good, while the quality of the reservoir in RF-4, RF-5 and RF-6 are poor. We conclude that RF-2 mainly corresponds to oil layers and oil-water layers, which are favourable target layers for exploration. This research is the first time to study the sedimentation and diagenesis and combine them to evaluate the reservoir physical properties and flow units in the South China Sea comprehensively.

An adaptive ensemble learning by opposite multiverse optimizer and its application in fluid identification for unconventional oil reservoirs

Unconventional reservoirs are rich in petroleum resources. Reservoir fluid property identification for these reservoirs is an essential process in unconventional oil reservoir evaluation methods, which is significant for enhancing the reservoir recovery ratio and economic efficiency. However, due to the mutual interference of several factors, identifying the properties of oil and water using traditional reservoir fluid identification methods or a single predictive model for unconventional oil reservoirs is inadequate in accuracy. In this paper, we propose a new ensemble learning model that combines 12 base learners using the multiverse optimizer to improve the accuracy of reservoir fluid identification for unconventional reservoirs. The experimental results show that the overall classification accuracy of the adaptive ensemble learning by opposite multiverse optimizer (AIL-OMO) is 0.85. Compared with six conventional reservoir fluid identification models, AIL-OMO achieved high accuracy on classifying dry layers, oil–water layers, and oil layers, with accuracy rates of 94.33%, 90.46%, and 90.66%. For each model, the identification of the water layer is not accurate enough, which may be due to the classification confusion caused by noise interference in the logging curves of the water layer in unconventional reservoirs.

Thin Reservoir Identification Based on Logging Interpretation by Using the Support Vector Machine Method

A reservoir with a thickness less than 0.5 m is generally considered to be a thin reservoir, in which it is difficult to directly identify oil-water layers with conventional logging data, and the identify result coincidence rate is low. Therefore, a support vector machine method (SVM) is introduced in the field of oil-water-dry layer identification. The basic approach is to map the nonlinear problem (input space) to a new high-dimensional feature space through the introduction of a kernel function, and then construct the optimal decision surface in the high-dimensional feature space and conduct sample classification. There are plenty of thin reservoirs in Wangguantun oilfield. Therefore, 63 samples are established by integrating general logging data and oil testing data from the study area, including 42 learning samples and 21 prediction samples, which are normalized. Then, the kernel function is selected, based on previous experience, and the fluid identification model of the thin reservoir is built. The model is used to identify 21 prediction samples; 18 are correct, and the prediction accuracy reaches 85.7%. The results show that the SVM method is feasible for fluid identification in thin reservoirs.

Genesis of bitumen and high resistivity water layer in Yanchang formation, Ordos Basin, China

ABSTRACT Many oil-bearing basins in the world have found high-resistivity water layers, which brings difficulties to the identification of oil-water layers and the exploration and development of oil fields. Yanchang Formation in Ordos Basin shows complex oil-water enrichment characteristics. Oil wells and water wells are interspersed on the plane. The resistivity of water layer is higher than that of oil layer, and it is difficult to explain it with the existing theory. Taking Chang 81 reservoir in Yuele area as an example, the electrical, physical and oil-bearing characteristics of two oil wells (B23 and Y73) and two water wells (Y79 and Y55) in Yuele area were comparatively studied by means of extraction experiment, GC-MS analysis, thin section observation, rock pyrolysis, resistivity, and physical property test. The results show that the high resistivity of water layer in Yanchang Formation of Ordos Basin is caused by biodegradation asphalt and heavy component of residual oil after structural adjustment. Asphalt filling is the main reason for high resistivity of water layer. The residual heavy hydrocarbon after structural adjustment aggravates the high resistivity of water layer.

Analysis of low-resistivity oil pay and fluid typing method of Chang 81 Member, Yanchang Formation in Huanxian area, Ordos Basin, China

The Chang 81 Member, Yanchang Formation in Huanxian area, Ordos Basin, China, is a tight sandstone dominated reservoir with low porosity and low permeability. The contribution of pore fluid to well logging response is greatly reduced, and the resistivity of oil layer and water layer are close, which bring great difficulties to the fluid identification and logs interpretation. In this paper, we first analyzed the genetic mechanism of the low-resistivity oil pay layer in terms of reservoir petrological characteristics, irreducible water saturation and formation water salinity. The high content of clay and complex pore structure resulting in high irreducible saturation which leads to additional conductivity; and the formation water salinity has great difference in this area. In order to reduce the influence of the formation water salinity difference on the fluid identification, we separated the study area into normal oil pay region and low-resistivity oil pay region based on the relationship between formation water salinity and formation resistivity. Then, double apparent formation water resistivity difference method, fluid identification factor cross-plot method, full hydrocarbon mud logging and well logging hybrid method and the normal distribution method were established in two different regions, respectively. The comparison of different fluid identification methods show that full hydrocarbon mud logging and well logging hybrid method is the best method for fluid typing of oil layer, oil-water layer and water layer. Double apparent formation water resistivity difference method is not effective to distinguish the oil-water layers from the water layers and oil layers, and the normal distribution method is not applicable for distinguishing the low-resistivity oil pay. In the case study, the above methods are together used to identify the fluids of the reservoirs, and the application result indicates that comprehensive analysis of multiple methods can effectively improve the accuracy of fluid typing.

Water spectrum method of NMR logging for identifying fluids

Abstract A new fluid identification method by constructing water spectrum based on NMR logging was put forward after the limitations of existing nuclear magnetic resonance (NMR) fluid identification methods were analyzed. At present, differential spectrum method (DSM) and shifted spectrum method (SSM) of NMR logging are commonly used fluid identification methods. Due to the effects of fluid properties and pore structures, however, their coincidence rates of fluid identification are lower. A new fluid identification method named water spectrum construction method was developed in this study. Based on the existing acquisition mode of NMR logging, T2 (transverse relaxation time) spectrum of long waiting time and long echo spacing in completely watered conditions was constructed from the T2 spectrum which was measured in the mode of long waiting time and short echo spacing. And then, the types of fluids in reservoirs were identified by comparing the measured T2 spectrum with the constructed water spectrum. This new method was applied in Nanpu sag, Bohai Bay Basin for identifying oil layers, oil-water layers, water layers, gas layers and low-resistivity oil layers. It is demonstrated that based on the water spectrum construction method, the coincidence rate of fluid identification caused by pore structures is increased and fluid identification capacity of NMR logging is improved. Water spectrum construction method is prospective for fluid identification and evaluation of complex reservoirs.

Diagenetic evolution and its effect on reservoir-quality of fan delta sandstones during progressive burial: Evidence from the upper part of the fourth member of Shahejie formation, Bonan sag, Jiyang depression

Petrographic analysis combined with various techniques, such as thin section identification, fluid inclusions, isotopic data, petro-physical property testing and oil testing results, was used to study diagenetic evolution and its effect on reservoir-quality of fan delta reservoirs of Es4s in the Bonan sag. The diagenesis is principally characterized by strong compaction, undercompaction, multi-phase of dissolution and cementation. Compaction played a more important role than cementation in destroying the primary porosity of the sandstones. The reservoirs have experienced complicated diagenetic environment evolution of “weak alkaline- acid-alkalinity-acid-weak alkalinity” and two-stage of hydrocarbon filling. The diagenetic sequences are summarized as “early compaction/early pyrite/gypsum/calcite/dolomite cementation→feldspar dissolution/the first stage of quartz overgrowth → early hydrocarbon filling→quartz dissolution/anhydrite/Fe-carbonate cementation→Fe-carbonate dissolution/feldspar dissolution/ the second stage of quartz overgrowth → later hydrocarbon filling→later pyrite cementation. In the same diagenetic context, the diagenetic evolution processes that occurred in different sub/micro-facies during progressive burial have resulted in heterogeneous reservoir properties and oiliness. The braided channel reservoirs in fan delta plain are poorly sorted with high matrix contents. The physical properties decrease continually due to the principally strong compaction and weak dissolution. The present properties of braided channel reservoirs are extremely poor, which is evidenced by few oil layers developed in relatively shallow strata while dry layers entirely in deep. The reservoirs both in the underwater distributary channels and mouth bars are well sorted and have a strong ability to resist compaction. Abundant pores are developed in medium-deep strata because of modifications by two-stage of acidic dissolution and hydrocarbon filling. The present properties are relatively well both in the underwater distributary channels and mouth bars and plenty of oil layers are developed in different burial depth. The present reservoir properties both in interdistributary channel and pre-fan delta are poor caused by extensively cementation. Small amounts of oil layers, oil-water layers and oil-bearing layers are developed in relatively shallow strata while dry layers totally in deep.

Organic geochemical identification of reservoir oil–gas–water layers in the Junggar Basin, NW China

The identification of reservoir oil–gas–water layers is a fundamental task in petroleum exploration and exploitation, but is difficult, especially in cases of complex hydrocarbon migration and accumulation. In such cases, hydrocarbon remigration and dysmigration take place very commonly, leading to the presence of residual or paleo-oil accumulations and layers, which cannot be easily identified or misinterpreted as oil layers by conventional logging and geophysical data. In this paper, based on a case study in the Luxi area of the central Junggar Basin, NW China, we seek to characterize such layers in terms of organic geochemistry. We suggest specific indicator parameters of organic geochemistry such as the chloroform bitumen content of reservoir extracts, which is usually >1.0% in oil layers. We explore the application of grains containing oil inclusions (GOI) (the ratio of mineral grains containing oil inclusions to the total number of mineral grains) for the identification of oil–gas–water layers in the Junggar Basin for the first time; this method has been used elsewhere. The maximum GOI values for the oil layers, oil–water layers, water layers and dry layers are >11%, 7%–11%, 6%–7% and <6%, respectively. In addition, gas layers and heavy-oil layers that are difficult to identify by conventional organic geochemical parameters were identified using biomarkers. The typical characteristics of the soluble reservoir bitumen in the gas layers include a much greater abundance of tricyclic terpanes (two times in general) relative to pentacyclic terpanes and a tricyclic terpane distribution of C20 > C21 > C23. In contrast, the typical characteristic of the heavy-oil layers is the presence of 25-norhopanes in reservoir bitumen extracts. These specific indicators can be applied in the Junggar Basin and in similar settings elsewhere.

Organic geochemical study of the “W” oil-gas field in the Yugoslav part of the Pannonian basin

The naphthenic crude oil from the “W” oil-gas field differs from almost all other, paraffinic base oils discovered until now in the Yugoslav part of the Pannonian basin. In order to explain this difference, some geological and organic geochemical investigations were carried out. On the basis of the bulk and biomarker parameters, determined in oil samples taken from 13 producing wells located at different parts of the field, it is concluded that the crude oil throughout the field has an almost uniform composition. However, some differences in alkane content, due to biodegradation and/or water washing were found. In the northeast part of the field where a tilted oil-water layers shows an active hydrodynamism, and where the temperatures are the lowest and pressure gradients the highest, the isoprenoids pristane and phytane were detected in trace amounts—indicating the most intensive biodegradation. In the central part, where a hydrocarbon accumulation exists in a Miocene reservoir, somewhat lower biodegradation was observed. The crude oil from this field has probably a mixed terrestrial-marine origin and is derived from argillaceous source rocks sedimented in marine delta or brackish lakes. Specific maturation parameters confirmed that the “W” crude oil has reached a quite uniform and significant thermal maturation. Considering maturation parameters such as ratios C 30 moretane/hopane and C 29 αα(20 S)/ αα(20 S)+(20 R) steranes, as well as the presence of oleanane, it could be proposed that the “W” oil originates from a Pre-Tertiary source rock, at least an Upper Cretaceous one, unlike most Pannonian basin oils which are Tertiary in age.