{'fraction_non_alphanumeric': 0.023001594169892964, 'fraction_numerical': 0.010749259849692553, 'mean_word_length': 5.033525693871943, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Direct hydrocarbon detection (DHI) techniques for prospective exploration targets in Chaoshan Depression

Located in northern South China Sea, Pearl River Mouth Basin is a large Meso-Cenozoic basin. And in southeasters part of the basin lies Chaoshan Depression, which is mainly a residual Mesozoic depression, with a construction of Meso-Cenozoic strata over 7000m thick and good hydrocarbon accumulation conditions. The selected DHI technique, amplitude attribute of -90 degree phase component derived by phase decomposition is employed to the zone of interest (ZOI) of B structure, Chaoshan Depression. And it is found that there are evident amplitude anomalies occurring around ZOI of structure B. Phase decomposition is applied to forward modelling results of the ZOI, and high amplitudes occur on the -90 degree phase component more or less when ZOI is charged with hydrocarbon, which shows that the amplitude abnormality in ZOIs of structure B is probably caused by oil and gas accumulation. Meanwhile, another DHI attribute of spectral decomposition peak amplitude is applied also to ZOIs of B structure, and there is also amplitude anomalies coincides with those of -90 degree phase component. The two DHI attributes’ coincidence strengthens the confidence of hydrocarbon accumulation prediction results.

Uplift is selected as a favourable exploration target, (Figure 1). The article will elaborate on petroleum potential and DHI analysis of the target.
Due to the shortage of available drilling data, reliable petro physical data and hydrocarbon information in the target area of the Chaoshan Depression (Figure 1), there is a large uncertainty in the direct prediction of oil and gas with conventional techniques. In order to make a breakthrough to the situation of Mesozoic oil and gas exploration in the Chaoshan Depression, this study applies peak frequency amplitude attribute and phase decomposition amplitude attribute analysis techniques for direct oil and gas detection.

Petroleum geology conditions in Chaoshan Depression
Drilled well LF35-1-1 confirmed that Chaoshan Depression is filled with Cretaceous continental sediments and Middle-Late Jurassic marine sediments, among which Middle-Late Jurassic marine sediments have good petroleum geological conditions [14][15][16].

Source rocks
The upper part of the Middle-Upper Jurassic strata revealed by well LF35-1-1 is composed of greyblack veined layered mudstone, argillaceous siltstone with siliceous rock, containing a small amount of micrite limestone; the lower part is grey-black veined layered mudstone and shale siltstone with sandstone, limestone. The mudstones are rich in organic debris. A set of river-lake sediments is generally made up of the Cretaceous strata. The upper part is a combination of purple mudstone, siltstone and sandstone with a small amount of marl, and the lower part is a combination of grey grained layered mudstone, siltstone and sandstone, containing some organic debris (Table 1).

Reservoir conditions
According to the formations revealed by well LF35-1-1 and seismic data interpretation, there may be two sets of reservoirs in the Chaoshan Depression. One set is the continental river-lake sandstone reservoir in Cretaceous, and the other set is coastal-shallow sea sandstone, slope fan and basin-bottom fan sandstone in the Upper-mid Jurassic, and sandstone and limestone reservoirs in transgresses system tracts.
The seismic profile ( Figure 2) across structure B shows that a set of limestone layers with stable thickness are distributed on the top of the Middle Jurassic (T j2 interface). A set of submarine fan sandstones advancing from SE to NW are developed on the top of the Upper Jurassic. The fan root (inner fan and levee deposits) is located on southeast of the profile. And mid-fan mainly develops in middle and northwest with mid-fan channel deposits, where there are sandstone reservoirs of the best properties. Mid-fan frontal deposits develop in northwest.
Vertically on the seismic profile, the down lap characteristics of the fore set can be clearly seen from SE to NW. The submarine fan can be divided into four stages, among which the second and third are most flourishing, and it is speculated that the sand content is the largest (Figure 3). Laterally, the results of seismic facies analysis show that the submarine fan is distributed in SE-NW direction, and structure B is located in braided channels and non-channel of the mid-fan area, where sandstone reservoirs of good quality develop.

Seal conditions
According to well LF35-1-1, multiple layers of mudstones encountered in this well are thick, and the maximum thickness of individual layer is more than 30 m. They should have good sealing capabilities and serve as important regional cap rocks in the area. In addition, there are many sets of mudstones in the Middle-Upper Jurassic, and these mudstones are mostly characterized of continuous or poorly continuous, medium or weak amplitude reflections on the seismic profile. According to the seismic profile, the distribution of these strata is stable, which can also serve as a regional cap rock.

Reservoir-forming model
According to cores of well LF35-1-1, the Cretaceous and Middle-Upper Jurassic are mostly composed of interbedded sand-mudstone in the Chaoshan Depression, which is very favourable to form good reservoir-seal assemblage. The main accumulation mode is source-reservoir coincidence and reservoir overlaying source (Figure 4).

Amplitude attribute of -90 degree phase component by phase decomposition
Phase decomposition is a unique and creative technology for DHI, originated by Castagna [17]. The acoustic impedance variations caused by pore fluids or lithology changes do not always produce very obvious amplitude anomalies on conventional post-stack profiles. By decomposing seismic traces into different phase components and suppressing unwanted phase components, it is possible to identify some subtle hydrocarbon, lithological changes or anomalous responses caused by sand channels, where significant amplitude anomalies occur at the expected phase components. This technology has become a very effective tool for special geological body identification and hydrocarbon detection [18][19].
Based on spectral decomposition [20], any seismic trace can be decomposed into 2D functions, amplitude spectrum and phase spectrum. A seismic trace S (t) is a 1D function whose amplitude varies with time. The amplitude spectrum A (f,t)is a 2D function whose amplitude varies with both time and frequency, and the phase spectrum θ(f, t) is a 2D function whose phase varies with time and frequency also. If we combine the amplitude spectrum and the phase spectrum into a function A (f, t) * cosθ (f, t), then a two-dimensional function representing reflection amplitude changes with phase and time can be obtained by the following formula: Where f1 and f2 are the starting and ending frequencies of the band. We define this function as phase gather. Any desired phase component in the specified frequency band is extracted as follows: Where θ1 and θ2 are the starting and ending phases of the phase band. In this way, the seismic reflection events with specific spectral characteristics can be suppressed or enhanced. The process of decomposing a seismic trace into a two-dimensional function of amplitude with time and phase is called phase decomposition. The amplitude change with time at a specific phase is referred to as a phase component. The seismic reflections caused by small impedance changes in the layer below the tuning thickness can be amplified, making specific geological features easier to discover. Any pair of reflection coefficients can be expressed as the sum of an odd and an even component. The reflection coefficients of even component are the same in size and sign, and those of odd component are the same in size and opposite in sign. As to an individual thin layer, the impedance variation changes the reflectivity of odd component, and thus the phase of the local reflectivity, while the even component remains almost unchanged. A phase filter can be designed and applied to seismic data based on the method described above, and the odd component may show an obviously strong amplitude anomaly at the location where weak amplitude appears originally. Especially in the case of low-impedance thin gas or light oil sands (bright spots), the amplitude abnormality corresponding to gas saturation will appear in the -90 degree phase component. Therefore, in practical applications, amplitude attribute analysis of -90 degree phase component is often used as a means for DHI.
Based on this technology, oil layer prediction is successfully carried out in EBS field in southeast Baghdad, Iraq. The primary layer of EBS oilfield for development is the Z5-Z6 sandstone reservoirs, which have obtained high-rate oil flow. However, the reservoir heterogeneity is strong, the sand-bodies are difficult to co-relate laterally, and the reservoir prediction is difficult. The controlling factors and the types of oil accumulation are unclear, and DHI is difficult. For this set of reservoirs, phase decomposition technology is used to for DHI, and a number of drilled wells confirm the reliability of the technology.
The figure on the left is the minimum amplitude map ( Figure 5). According to this map, the reservoir distribution is stable with a good continuity. Using phase decomposition technique, the -90 degree phase amplitude attribute map is extracted ( Figure 5 on the right). According to the map, the reservoir range is obviously reduced, and at the same time, the local continuity is deteriorated, and the oil accumulation range is significantly reduced. Well EB-B is the first newly drilled based on the prediction map and post-drilling results are consistent with the predicted results. The oil layer thickness is about 12m, equivalent to that of the early well EB02., Other four wells (Z-1, Z-2, Z-3 and Z-4) are drilled one after another afterwards, among which Z-2 and Z-3 are better in production. However Z-1 and Z-4 are located at the edge of the anomaly on the attribute map, and only the 2-3m oil layer is encountered with poor oil-bearing property, although the sand reservoirs are well developed.  Figure 5. Comparison of the minimum amplitude map and -90 degree phase amplitude map of layer Z5-Z6 in EB02 well area.

Spectral decomposition peak amplitude analysis
The peak amplitude attribute is an important one by spectral decomposition, and it is the maximum value of the reflection coefficient amplitude spectrum after eliminating the tuning effect [21]. Over the time domain amplitude attribute, it has the advantage to reflect the changes reservoir properties and hydrocarbon accumulations more realistically. According to Castagana, any pair of reflection coefficients can be decomposed into odd and even parts ( Figure 6). The odd component ro is composed of reflection coefficients with opposite amplitudes and polarities, while the even odd component re is composed of reflection coefficients with equal amplitude values and the same polarities. With Fourier transform, the reflection coefficient expression is represented in frequency domain: Then the amplitude spectrum expression of the reflection coefficient pair is obtained: The peak amplitude attribute is the maximum value of the amplitude spectrum expression. From the calculation formula of the amplitude spectrum, it can be seen that for a set of layers, changes in fluid or lithology or reservoir properties will increase absolute values of the top and bottom reflection coefficients, which is reflected by the significantly increased peak amplitude attribute [22].
In practical application in this area, the peak amplitude attribute can be obtained to directly detect reservoir and oil-bearing properties. The original seismic profile and peak amplitude attribute are displayed in Figure 7 and 8  the conventional amplitude profile since the amplitude of the brine layer and the oil layer is not much different. While on peak amplitude attribute profile, the oil layer is characterized of strong amplitude and the oil layer is characterized by weak reflection. The peak amplitude attribute can be used to effectively distinguish oil layers.

Selected DHI techniques
-90 degree phase component technology for DHI has achieved excellent results in practical applications. In the exploration target area of Chaoshan Depression, there is no well drilled available. It is wondered that the technique of phase decomposition and -90 degree phase component DHI can achieve good results. We conduct forward modeling using the reservoir properties adjacent to the Chaoshan Depression to see what will happen to the amplitude attribute of -90 degree phase component if target reservoir contains oil and gas, and whether it can predict oil and gas.

Model establishment
The main purpose of establishing the forward model is to investigate the response characteristics of the amplitude of the -90 degree phase component to oil, gas or brine. The forward modeling is implemented using Leonardo technology and then the response characteristics of the two attributes with different pore fluids are summarized.
In practical work, there are three types of impedance changes for reservoir and its surrounding rocks: ① The impedance of surrounding rocks is higher than that of oil, gas or brine reservoirs, and oil-gas reservoirs are characterized of bright spots; ② The impedance of surrounding rock is lower than that of oil, gas or brine reservoirs, and oil-gas reservoirs is characterized of dim spots; ③ The impedance of surrounding rock is lower than that of brine-bearing reservoirs, but higher than that of oil-gas reservoirs, which shows polarity reversal (Figure 9).  According to the above assemblage patterns for reservoir and the surrounding rocks, and the drilling data of well LF35-1-1 in northern slope, the geological model is established firstly according to the lithology combination and velocity changes in the target area. The upper Jurassic stratum is the target zone, with a depth of about 1700-2300 meters. The lithology of the overlying rocks includes siliceous rock, mudstone, and argillaceous siltstone; and the reservoir lithology includes sandstone, siltstone; and underlying rocks is limestone. Various lithology and its velocity are as follows in Table 2. According to the velocity characteristics of siliceous rock and mudstone, the following three models are established (Figure 10-12). For No. 1 model, the overlying rock is siliceous rock with velocity of 5350m/s, which is greater than that of the sandstone reservoir. For No. 2 model, the overlying rock is mudstone, and its velocity of 4200 m/s is less than that of sandstone reservoir. As to No. 3 model, the overlying rock is a combination of siliceous and mudstone with the velocity of 5100m/s, which is the average velocity of siliceous rocks and mudstones, and the caprock velocity are close to that of the sandstone reservoir.

Seismic modeling for DHI in Chaoshan Depression
The forward seismic profiles corresponding to the three models are displayed in  According to the synthetic profiles by forward modeling, the reservoir top of model 1 is a trough, and the amplitude of oil/gas-bearing case is significantly higher than the brine-bearing reservoir. The reservoir top of model 2 is a peak reflection, and the amplitude when oil/gas-bearing case is significantly lower than brine-bearing case reservoir. The brine layer top of model 3 is a peak reflection, and the gas layer is a trough. The amplitude of the brine layer is slightly different from the gas layer, and the oil layer is a peak of weak amplitude. On seismic sections, it difficult to directly identify the oil and gas layer.   The amplitude attribute of the -90 degree phase component is extracted on forward synthetic seismic data ( Figure 16-Figure 18). On -90o phase component profiles of the three models, it is similar that the gas layer has the strongest amplitude and brine layer the weakest amplitude, and the oil layer amplitude between gas and brine.
In Table 3, the absolute amplitude values of synthetic seismic data and -90 degree phase attributes are read from the three models of brine, oil and gas respectively. And amplitude values of -90 degree phase attributes are cross-plotted against those of synthetic seismic data ( Figure 19). On the crossplotting, there is not a uniform threshold to identify oil and gas layers for original synthetic seismic amplitude only, while for -90 degree phase amplitudes, uniform thresholds can be seen between brine and oil and gas. The amplitude value above 0.0005 is gas layer, and the amplitude between 0.0035-0.0005 oil layer, and the amplitude less than 0.00035 brine layer.      Figure 19. Cross plotting for synthetic amplitudes and its phase component of 3 models.

DHI for identification of exploration targets in Chaoshan Depreession
Well LF35-1-1 confirms that there are two sets of reservoir-cap rock assemblages in the Chaoshan Depression. One is interbedded sandstone and mudstone in Cretaceous, the other is the Middle-Upper Jurassic. And the latter is the main target interval for oil and gas exploration. For this main exploration target, the reservoir occurrence is analyzed by extracting conventional seismic amplitude attributes. DHI is performed using -90 degree phase component amplitude attribute analysis and spectrum decomposition peak amplitude attribute analysis respectively, so as to verify the two prediction results mutually and increase DHI confidence.

Analysis of conventional seismic amplitude attributes
Seismic amplitude attributes can indicate reservoir property variations to a certain extent, so the seismic amplitude attributes are usually applied to qualitatively analyze reservoir development. The seismic section along one favorable exploration target in the Chaoshan Depression is shown in Figure 20. On the section, the ZOI of Upper Jurassic shows strong reflections locally, which is probably the seismic response of the reservoir with better properties. By extracting its amplitude attribute, the lateral distribution range of the strong reflection of ZOI is preliminarily delineated, which probably corresponds to the range of favorable reservoirs of ZOI ( Figure 21).

-90 degree phase component amplitude attribute analysis for DHI
The -90 degree phase component amplitude attribute by phase decomposition for DHI is used to study on the seismic section where there are strong reflections around ZOI, to clarify the distribution of abnormal amplitudes of the -90 degree phase component attribute, and then to identify its possible oil and gas distribution scope.
A comparison between the original seismic profile and the -90 degree phase component profile is illustrated in Figure 22. From this comparison, strong amplitude reflections on the -90 degree phase component profile do not coincide with all those on the conventional seismic profile, and some strong amplitudes are no longer strong on -90 degree phase component profile. According to the previous forward modeling results, strong amplitudes on the -90 degree phase component profile is probably caused by presence of oil and gas. By extracting the amplitude attribute of the -90 degree phase component, the possible oil and gas distribution range is identified in Figure 23 on the right. The comparison between the attribute map of conventional amplitude and that of -90 degree phase component along ZOI is illustrated in Figure 23. There are clusters of strong amplitude characteristics on both maps. However, the strong amplitude range on the -90-degree phase component map is reduced, and the boundary of the strong amplitude range is clearer, and it is more consistent structural contours. Obviously stronger amplitudes of -90-degree phase component especially occur on structural highs (Figure 23, on the right). Therefore, it is speculated that the oil and gas accumulation is extremely likely to occur in the areas with high amplitudes.

Spectral decomposition peak amplitude analysis for DHI
The peak amplitude attribute is sensitive to reservoir oil and gas. By comparing the original seismic section with the peak amplitude attribute section, it is found that oil/gas-bearing reservoirs show strong amplitude characteristics on the peak amplitude attribute section. A comparison between original 13 seismic section and the peak amplitude attribute section of favorable exploration targets in Chaoshan Depression is illustrated in Figure 24. The peak frequency amplitude section shows strong reflections locally, but the continuity varies slightly on the top. By comparing peak amplitude attribute map with the original amplitude attribute (Figure 25), the intensity of the peak amplitude attribute anomalies has slight changes laterally, and their continuity in anomalous areas fluctuates slightly. It also indicates that hydrocarbon accumulation scope is smaller than reservoir distribution range.

Conclusions
For exploration frontier areas under study, there is much uncertainty to apply conventional seismic attribute analysis for identifying oil and gas prospective targets due to few wells available. Fortunately, the joint application of two methods, -90 degree phase component amplitude attribute analysis by phase decomposition and peak amplitude attribute by spectrum decomposition, is an effective alternative for DHI in these areas. Together with its forward modeling analysis, -90 degree phase component amplitude attribute analysis method is used to predict reservoir oil and gas bearing property of the structure B in Chaoshan Depression, which indicates that there are probably oil-bearing layers in Upper Jurassic reservoirs. At the same time, it is supported by peak amplitude attribute analysis, and thereby the confidence of DHI results is enhanced.