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ORIGINAL PAPER
Jia-Bei Wang1 & Han-dong Huang2,3
Received: 16 May 2016 /Accepted: 7 September 2016 /Published online: 5 October 2016# Saudi Society for Geosciences 2016
Abstract In the early stage of deep-water exploration, theresearch about subtle trap prediction is mostly confined totechnologies such as seismic attribute, due to which ambiguityand uncertainty in the results can occur. Therefore, in thispaper, a new process of identifying deep-water reservoirs isproposed. First, strata slice technology is adopted to guide theanalysis of seismic facies and preliminary recognition of thedistribution and scale of geologic bodies; then based on theseismic facies analysis, the reservoir is depicted in detail withthe application of seismic phase-controlled nonlinear randominversion; and finally, the comprehensive analysis of variousfeatures like strata slices, seismic facies, and seismic inversionare used to predict the favorability of the reservoir. The resultsshow that the application of this method not only successfullypredicts the distribution of the reservoir but also provides areliable basis for future well drilling.
Keywords Deepwater . Early stage of exploration . Strataslice . Seismic facies . Seismic phase-controlled nonlinearrandom inversion . Reservoir prediction
Introduction
The exploration and development of deep-water oil and gashas become an important new field to increase the reserve andproduction of oil-gas resources around the world. Meanwhile,it has also become a development trend and new highlight inthe exploration and development of offshore reservoirs (Heet al., 2006; Ma et al., 2008). There are some successful ex-amples in the conventional technologies like seismic faciesand seismic attribute analysis in deep-water reservoir predic-tion (Saggaf et al., 2003; Deng et al., 2007; Li et al., 2008;Zhang et al., 2009; Marroquin et al., 2009a, b; Manzi et al.,2012; Torrado et al., 2014). However, these methods presentcertain difficulties in reservoir prediction in the early stage ofdeep-water exploration. The exploration level in the deep-water area is relatively low and the geological understandingis not clear. As a result, the credibility of using the aboveconventional methods to predict reservoirs is doubted due tothe lack of wells in the study area as well as the lack of infor-mation on the ground outcrops that can be observed and de-scribed directly, so the sedimentary period of the reservoircannot be depicted precisely and also cannot be predictedeffectively. Thus, reservoir prediction in the early stage ofdeep-water exploration has become a bottleneck that restrictsthe exploration and development of deep-water oil and gasreservoirs.
Based on the above-mentioned problems, a new idea inreservoir prediction in the early stage of deep-water explora-tion is proposed in this paper. First, the spatial distribution ofthe target geologic body is preliminarily identified with strataslices and seismic facies; second, a seismic phase-controllednonlinear random inversion method is applied to the depictionof the target geologic body, the description of the reservoircharacterization, and the prediction of the reservoir. Fromthe good results achieved in the application of our method to
* Jia-Bei [email protected]
1 China University of Geosciences, Beijing 100083, China2 State Key Laboratory of Petroleum Resource and Prospecting, China
University of Petroleum, Beijing 102249, China3 Research Center of Enhance Oil Recovery, China University of
Petroleum, Beijing 102249, China
Arab J Geosci (2016) 9: 662DOI 10.1007/s12517-016-2678-1
Seismic facies and inversion analysis for reservoir predictionin the early stage of deep-water exploration
a deep-water area in West Africa, it can be seen that thismethod has broad application prospects in reservoir predictionin the early stage of deep-water exploration.
Geologic setting
The study area is located in the Congo basin on the WestAfrican passive continental margin, with an area of 481 km2
and a water depth of 350–1200 m. Two approximate parallelnorth-south main faults are developed in the block, with atectonic framework of Bbelts in west-east^ and Balternationbetween uplifts and depressions,^ as is showed in Fig. 1.Figure 1 is the T0 map of the Tertiary top layer, and the irreg-ular polygon is the scope of the study area. Furthermore, thetarget layer is Tertiary, which contains a developed turbiditefan in a deep-sea sedimentary background. According to se-quence stratigraphic classification results, the Tertiary depositis roughly divided into five periods of turbidite fans, with thefifth period of turbidite fan (A5) being the main target sand.This paper mainly focuses on the study of A5-period reservoirprediction.
The exploration level in the study area is relatively low andthere are only two wells there: Nkam-1 and Libm-1, and nei-ther of them drilled out the Tertiary deposit. In addition, thedrilling results of the two wells show that the A5-period sandin the Tertiary has a good oil and gas potential. In addition, theexploration evaluation in adjacent regions indicates that thespatial distribution of reservoirs is mainly controlled by thelithology of the whole regional background. Therefore,depicting the spatial distribution and thickness of the turbidite
fan in detail is key to predicting a reservoir in the A5-phaselayer.
Analysis of the seismic response
The analysis of the turbidite fan’s seismic response charac-teristics is a precondition of reservoir prediction in thestudy area. Seismic attribute and seismic facies are twotypical reaction ways of determining seismic responses.Seismic attributes can reflect relevant information like li-thology and reservoir physical properties (Wang et al.,2013; Li et al., 2015a, b; Wang et al., 2015), and seismicfacies are the geometric characteristics of the sedimentaryfacies in the seismic section, with the ability to make lateralseismic information and 3D space observations (Fomel2007; Yang et al. 2009; Zhu and Zhao 2009; Zhang et al.2010).
Typical seismic reflection characteristics
In the analysis of seismic facies, the external geometric featuresand internal reflection configuration of seismic reflection withtypical characteristics are considered, and the parameters such ashigh or low seismic amplitude and continuity are used to divideand name the seismic facies. By checking the seismic sectionsone by one, four types of seismic reflection characteristics areidentified in the Tertiary system: incised filling, chaotic facieswith strong amplitude; flat, lenticular, chaotic facies with strongamplitude; sheet-like facies with strong amplitude; and sheet-like facies with weak amplitude (Fig. 2). These four kinds ofseismic facies are distributed in different locations of the studyarea. The corresponding interpretations are shown in Table 1.
Strata slice
Strata slice, which can remove the influence of tectonic defor-mation, is significant because it exhibits isochronism andchronological geology (Zeng et al., 1998a, b; Lei et al.,2010; Li et al., 2015a, b). Therefore, strata slice can reflectthe depositional configuration of an ancient sedimentary sur-face and the evolution of the sedimentary environment, whichplays an important role in recognizing the turbidite sand bodyof the turbidite fan in different periods. In the study area, thereis a large difference between the seismic responses of thedeep-water turbidite sand group and the deep-water mudstone.Specifically, the most typical seismic response characteristic isthat the turbidite sandstone is strong in amplitude, changesrapidly, and has poor continuity, while the deep-water mud-stone is weak in amplitude, is relatively stable, and has goodcontinuity in the seismic section. So the depositional config-uration is clearly reflected in the strata slice.
Along the top of the Tertiary A5, strata slices of differ-ent periods are extracted, as shown in Fig. 3. According to
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the seismic reflection, the characteristics of the red partwith relatively high amplitude clearly show the distribu-tion range of the feeder channel and turbidite fan.Through these strata slices, a rough interpretation is madeas follows: there are three feeder channels stretching fromsoutheast to northwest in the study area during the A5period (shown with black arrows). The slice in Fig. 3ais from the relatively earlier strata than Fig. 3b.Comparison of the two slices indicates that the channelschanged over time. By comparing the slices and the T0structural map (Fig. 1), it can be seen that the three feederchannels spread and form the main body of the turbiditefan in the sag under the fracture slope break belt in the
center and in the north, which belong to the typical deep-water turbidite fan of feeder channel-fracture slope breaktype. Through the analysis of strata slices of feeder chan-nels in different periods, it seems that the feeder channelshave been rechanneled, which may have resulted from thesuperposition and sedimentation of multiphase channels.
Seismic facies plane distribution
Under the macro-guidance of strata slices, the types of seismicfacies can be identified in the seismic profile from north tosouth and an A5-period seismic facies map can be drawn, asshown in Fig. 4a. In the Legend, C, A, B1, and B2 represent
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different seismic facies, with each corresponding to dif-ferent sedimentary facies interpretations. The locationsof the two wells show incised filling, chaotic facies,which can be identified as feeder channel deposits.Three feeder channels developed in the study area.Between the feeder channels, an overbank deposit grad-ually develops. Meanwhile, by comparing this facieswith the classic deep-water fan Walker model (Fig. 4),the whole facies can be further divided into three
subfacies: an inner fan, a middle fan, and an outerfan. Three feeder channels extend from the A5-periodturbidite fan from southeast to northwest, stretch at thehollow under the fracture slope break belt, and form themain body of the deep-water turbidite fan. Then, themain channel of the turbidite fan splits into multiplebranch channels and disappears in the northwestern partof the study area. At the same time, a large area ofinter-channel deposits is also formed.
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The seismic phase-controlled nonlinear randominversion
Through the analysis of seismic facies, the distribution of possi-ble reservoirs in a turbidite fan can be determined from a macro-scopic perspective. However, as the seismic profiles always havelow resolution, it is difficult to find the microscopic features of areservoir in a turbidite fan. Nevertheless, higher precision inver-sion profiles can be obtained through seismic inversion, so theuse of phase-controlled nonlinear random inversion (Huanget al., 2007) was introduced into our research, which can revealthe characteristics of sand bodies in seismic profiles. Thus, it willbe easier to predict the internal structure and sedimentary periodof the turbidite sands on the seismic inversion profiles.
Introduction to the seismic inversion method
The seismic phase-controlled nonlinear random inversionmethod adopted in this paper was put forward by HuangHanDong in 2007 (Huang et al., 2007). An important featureof this method is that it uses geological analysis of seismicfacies as an important constraining condition in the reservoirseismic inversion. As a result, the inversion results can betterreveal the features of the target geologic body, which is moreconducive to the recognition of a reservoir. The inversionmethod is mainly divided into two steps: first, the interfaceof the seismic facies is determined according to the seismicdata, and the macroscopic phase-controlled model is set up;and second, under the constraints of the logging data, a
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Fig. 3 The strata slices of the Tertiary A5 deposition period. a A5 top-down 90 ms; b A5 top-down 30 ms
Table 1 Characteristics andinterpretations of the Tertiary A5-period typical seismic facies
Typical seismicfacies
Seismic reflectionfeature
Sedimentaryfaciesinterpretation
Distributionlocation
Incised filling,chaotic facieswith strongamplitude
Obvious incised filling characteristics,strong event amplitude, and badcontinuity; the stratum is partiallysuddenly thickened
Feederchannel
The slope in thesoutheast part of thestudy area
Flat, lenticular,chaotic facieswith strongamplitude
Flat, lenticular, thick in the middle,thin on both sides; strong eventamplitude, bad continuity, and shortextension
Turbiditychannelfrontalfacies
The sag under thefracture slopebreak belt
Sheet-like facieswith strongamplitude
Parallel or nearly parallel event; strongamplitude, low frequency, and goodcontinuity
Channelexcessiveshorefacies
On both sides of thefeeder channel
Sheet-like facieswith weakamplitude
Parallel or nearly parallel event; weakamplitude, low frequency, and goodcontinuity
Bathyal-abyssalfacies
In the South of well Libm-1/The East part of thestudy area
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nonlinear random algorithm is applied to successive extrapo-lations and inversions.
Seismic phase-controlled constraint extrapolation process
Seismic facies are the reflection of the sedimentary facies on theseismic sections. The establishment of an isochronous strati-graphic framework is a prerequisite for building a seismicphase-controlled model. Through well to seismic calibration onseismic sections, the stratigraphic framework of the A5-phasefacies interfaces can be set up. Combining the stratigraphicframework with the distribution of the seismic facies (Fig. 4), afacies-controlled macroscopic model is established to constrainthe seismic inversion.
In the phase-controlled constraint extrapolation calculation,a polynomial phase-time fitting method is adopted to establishthe extrapolation relationships between seismic traces. Thespecific procedure is that in the time window controlled bythe facies interfaces starting from the wells, the prior modelparameter vectors obtained from the logging data are extrap-olated along the polynomial fitting phase change directions toparticipate in the next constrained seismic trace inversion(Huang et al., 2009a, b).
Assuming that N is a given positive integer, then f(−N),f(−N + 1),…, and f(N) are given numerical values, and a 2Npolynomial can be used to fit the data f(x), which is shown asfollows:
f xð Þ ¼ c0p0 xð Þ þ c1p1 xð Þ þ…þ cnpn x:ð Þ ð1Þ
Where pi(x)(i = 0, 1, 2, …, n) is a polynomial of degree nfor x, which satisfies the following equation:
p0 xð Þ ¼ 1Xpk xð Þpm xð Þ ¼ 00
(ð2Þ
and pk(x) and pm(x) are mutually orthogonal (k ≠ m).Since p0(x) = 1, the total pi(x) (i > 0) can be calcu-lated through the recurrence method. In general, therequired precision can be achieved by cubic polynomi-al fitting of the seismic signal using the followingequation:
ck ¼X N
−Npk xð Þ f xð Þ=
X N
−Np2k xð Þ f xð Þ k ¼ 0; 1; 2…;N ð3Þ
Nonlinear random inverse algorithm
The stochastic simulation method takes into full considerationthe random distribution of the underground geologic body,which results in the inversion results being more in line withthe actual geologic conditions. The specific implementation ofthe algorithm is as follows: the model parameters (velocity ordensity curves, etc.) are taken as regionalized variables torandom simulation processing; then the variation function isused to establish the statistical correlation function betweenthe model parameters. The regionalized variable is composedof discrete points on the well logging curve vector; the vector
Fig. 4 The comparison chart of the A5-period seismic facies and the deep-sea fan Walker model. a A5-period turbidite sand seismic facies map; bWalker deep-sea fan pattern
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dimension is determined by the log curve samples within thefacies interfaces. Based on their statistical relationship, themodel parameter variables are regarded as random variableswith a Gaussian distribution, as a Gaussian random field onthe space, which can be described by a Gaussian randomfunction.
Based on the nonlinear optimizing inversion of the seismictraces, the objective function of the relationship between theseismic trace amplitude and the impedance can be defined asEquation (4) (Yang, 1993, 1997), namely the minimum valueof the object function is solved by the least squares method asfollows:
f Vð Þ ¼X n−1
i¼0SΔi −Di� �2
→min: ð4Þ
Where V is the velocity, SΔi is the model response, D is thereal seismic record, and n is the number of seismic points. Ifdensity is assumed to be a constant, the inversion for theimpedance can be converted into an inversion for velocity.
The SΔi −Di� �
term is expanded by a Taylor series based onthe idea of a generalized linear inversion (Cooke andSchneider, 1983) as follows:
SΔi −Di� � ¼ Si−Di þ
X n−1
k¼0ΔVk
∂Si∂Vk
þ 1
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k
X n−1
k¼0
∂2Si∂Vk∂V j
� �þ⋯; ð5Þ
where Si is the corresponding synthetic seismic record com-puted from the initial velocity model. For easy estimation, thehigher-order terms in Equation (5) were omitted by Cook andSchneider (1983) and the nonlinear problem became linear-ized. Through Cook and Schneider’s omission of the higherterms, the solution speed was improved, but the accuracy wasreduced, which does not help in the case of a thin-layer inver-sion. As a result, we retain the square term and omit the higherterms, giving the following equation:
SΔi −Di� � ¼ Si−Di þ
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k
X n−1
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� �:
ð6Þ
ΔV is the model parameter perturbation value. Then it ispossible to solve the first-order ΔV derivation and the ΔVexpression can be estimated (Huang et al., 2009a, b). The finalinverse velocity can be obtained by the iteration of
Vmþ1 ¼ Vm þΔVm; ð7Þwhere m is the number of iterations.
Sensitivity analysis
Before seismic inversion, a sensitivity analysis of the loggingcurves is indispensable. Different logging curves have differ-ent responses to the lithology and petrophysical parameters.
The purpose of the sensitivity analysis is to find the relation-ship between the logging curves and the lithology, physicalproperties, and oiliness. So it is important to analyze thepetrophysical features of target formations from well log databefore applying the seismic inversion. Figure 5 is a DT andGR cross-plot of the two wells in the A5-phase layer (blue forshale, red for sand). From Fig. 5, we see that the velocity andGR curves can distinguish sand from shale very well in theA5-phase layer. In most cases, the velocity of the sand ishigher than that of the shale. However, due to the presenceof gas and oil, it is interesting to see that the sand is actuallyslower than the shale in this case. This provides good guid-ance for the next inversion process.
Analysis of the inversion results
The inversion results have a high level of accuracy and canfurther divide the fan
Well Libm-1 encountered three sets of turbidite sandstone inthe A5 period during drilling. The three sets of turbidite sand-stone exactly correspond to the three sets of low speed sand-stone reservoirs in the inversion profile (I, II, and III in Fig. 6),and through inversion, each set of sand groups is divided intoone to three layers in the inversion section (Fig. 6). Accordingto the sedimentary facies interpretation in Fig. 4a, well Libm-1is located in the feeder channel. Combining this with the in-version results, we can see that the reservoir in the feederchannel is composed of different sets of sands, so the feederchannel has a great exploration potential. Thus, it can be seenthat the inversion results not only coincide well with the log-ging data with a high level of accuracy but can also furtherdivide the whole sand body in the turbidite fan into severalsets of sands. The inversion results can image the thin reser-voir very well.
The inversion results conform to the sedimentary background
The random characteristics of the geologic background arefully considered in the phased-controlled nonlinear random
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inversion, which makes the inversion results more in linewith the sedimentary background. Both the drilling resultsand the seismic facies show that well Libm-1 directlyintersects the three sets of feeder channel sand in the A5period. The features of the sand in the A5 period are inthe shape of a lens, with a relatively chaotic structure(Fig. 7). Furthermore, the sand body reveals a large thick-ness, with relatively bad continuity. In addition, the reser-voir system consists of three sets of thick reservoirs,which reflects the superimposed characteristics of the
multiphase feeder channels. In conclusion, the inversionresult can distinguish the multi-phase deposits inside thefan, and the sand in the inversion profiles is not only inaccord with the sedimentary background but also depictsthe features of the reservoir in the turbidite fan clearly.
Turbidite sand reservoir prediction
Based on the inversion results, the different sets of sand in theA5-period turbidite sand can be laterally traced and
Fig. 6 Comparison of the inversion profile and the logging data (Cdp 1802)
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interpreted; then the whole sand thickness can be mapped. Asshown in Fig. 8 (Fig. 8 is the map of the whole A5-periodsandstone reservoir thickness, and the upper red polygonarea is the study area), the thicknesses of the sand inLibm-1 and Nkam-1 are 51 and 36 m, respectively; the sandbody is thick in the eastern and southern parts of the depositbut thin in the western and northern parts of the deposits. Anarrow strip channel sand extending from the south to thenorth, which passes Nkam-1, reflects the sedimentary char-acteristics of the turbidite sand feeder channel; the relativelythick irregular lobate sand body in the east reflects the sed-imentary characteristics of the middle fan of the turbiditefan; while the sandstone reservoir in the west, which is un-developed and becomes thinner from south to north, reflectsthe sedimentary characteristics of the outer fan.
Conclusions
In the early stage of deep-water exploration, in order to avoidthe problems of multiple solutions and uncertainty presentwith the use of conventional methods to predict subtle trap,the seismic phase-controlled nonlinear random inversionmethod is adopted in this paper based on seismic attributeand seismic facies to depict the turbidite sand reservoir. Thefollowing conclusions are drawn from the application of thismethod to the study area in West Africa:
(1) The deep-water turbidite sandstone reservoir in the studyarea has the characteristics of strong amplitude, rapidchange, and discontinuity. However, the amplitude ofdeep-water mudstone on the seismic profile is weak
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L1500
L1500
L2000
L2000
L2500
L2500
L3000
L3000
T1000
T1000
T1500
T1500
T2000
T2000
T2500
T2500
T3000
T3000
T3500
T3500
T4000
T4000
T4500
T4500
T1000
T1000
T1500
T1500
T2000
T2000
T2500
T2500
T3000
T3000
T3500
T3500
T4000
T4000
T4500
T4500
L2800
L2800L2600
L2600L2400
L2400L2200
L2200L2000
L2000L1800
L1800
L3000
L3000
L2800
L2800L2600
L2600L2400
L2400L2200
L2200L2000
L2000L1800
L1800
L3000
L3000
T3800
T3800
T3600
T3600
T3400
T3400
T3200
T3200
T3000
T3000
T2800
T2800
T2600
T2600
T2400
T2400
T2200
T2200
T2000
T2000
T1800
T1800
T1600
T1600
T1400
T1400
T1200
T1200
T3800
T3800
T3600
T3600
T3400
T3400
T3200
T3200
T3000
T3000
T2800
T2800
T2600
T2600
T2400
T2400
T2200
T2200
T2000
T2000
T1800
T1800
T1600
T1600
T1400
T1400
T1200
T1200
3636
5151
4848
3030
2121
2424
6363
5151
3939
5454
Nkam-1
Libm-1
Fig. 8 Reservoir thickness map for the A5-period
Arab J Geosci (2016) 9: 662 Page 9 of 10 662
and stable and has a better continuity as well. In addition,the general form of the turbidite fan and the evolution ofthe sedimentary environment can be roughly reflected bystrata slices.
(2) According to the features of the seismic reflection, fourtypes of typical deep-water turbidite fan seismic facies inthe Tertiary target layer can be identified, including theincised filling, chaotic facies with strong amplitude; theflat, lenticular, chaotic facies with strong amplitude; thesheet-like facies with strong amplitude; and the sheet-like facies with weak amplitude.
(3) Both the strata slices and the seismic facies show that thefeeder channel stretches from the south to the northwestof the study area and forms the main part of the turbiditefan under the fracture slope break belt in the center of thestudy area, which belongs to a typical deep-water turbi-dite fan of the feeder channel-fracture slope break type.
(4) Under the condition of a sedimentary setting of a deep-water turbidite fan, in this paper, the sedimentary char-acteristics of the seismic facies are analyzed comprehen-sively according to the geologic information reflected bythe strata slices and the logging data. Then, based on theclassical deep-water fan Walker model, the turbidite fanis further divided into three parts: the upper fan based onthe feeder channel, the middle fan under the slope breakbelt, and the outer fan in the front.
(5) Seismic phase-controlled nonlinear random inversionnot only has high precision but can also distinguish themultiphase sand inside the fan. In addition, this methodalso fully considers the random characteristics of theunderground geology, and the inversion results are inaccordance with the sedimentary background, whichprovides good guidance for reservoir prediction.
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