Direct Hydrocarbon Indicators based on Long short-term memory neural networkLuiz Fernando Santos∗, Reinaldo Mozart Silva and Marcelo Gattass, Computer Science Department andTecgraf/PUC-RIO, Aristofanes Correa Silva, Electrical Engineering Department and NCA/UFMA

SUMMARY

Direct hydrocarbon indicators (DHIs) is a seismic anomalythat may indicate a possible reservoir. However, the task offinding a DHI anomaly in the seismic data is arduous consid-ering that in most of the cases, this indicator is hard to sight ina large seismic data. Deep neural networks algorithms havebeen solving similar human-intensive and time- consumingproblems with increasing speed and accuracy. In this paper,we propose a novel method to detect possible DHIs on seismicdata applying a Long Short-Term Memory (LSTM) neural net-work on the seismic trace scale. To test our proposal, we usethe Netherlands Offshore F3-Block, a known open 3D seismicdata with a significant amount of gas. The results show thatthe proposed DHIs achieving a 97% accuracy, 95% sensitivity,97% specificity, and 99% AUC.

INTRODUCTION

Seismic reflection surveying is a well-known method to ob-tain subsurface information in the oil and gas exploration in-dustry. By analyzing the seismic data, an expert may getstructural and stratigraphic geometric features and, with addi-tional sources of geophysical information, possible locationsof hydrocarbon accumulations and its amount. However, theseismic data interpretation procedure is a time-consuming andhuman-intensive task, mainly due to the constant increase inthe amount of data to analyze and the tied deadlines demandedby the industry.

In this sense, to deal with the paradox of industry demandand increasing data volumes, the emerging technology calleddeep learning can assist geoscientists in the seismic interpreta-tion task and in different segments of the exploratory pipeline.Deep Neural Networks (DNN) can provide significant ad-vances in the seismic area by increasing the speed and accu-racy of data analysis. For instance, works like Pochet (2018),Araya-Polo et al. (2017), Huang et al. (2017), and Di et al.(2018b) presents successful uses of the Convolutional Neu-ral Network (CNN) to detect faults in seismic data. Di et al.(2018a), Shi et al. (2018), and Waldeland and Solberg (2017)use CNNs for a different approach, tacking the salt dome delin-eation in 3D seismic data. However, for the best of our knowl-edge, no work approaches the application of DNNs regardingthe identification of possible direct gas signature in seismicimages.

In this context, we propose a novel approach to detect directhydrocarbon indicators (DHI) in seismic data using the seis-mic trace and a Long Short-Term Memory (LSTM) neural net-work. To develop the model we used geoscientists input forthe gas identification on the public seismic dataset NetherlandsF3-Block dGB Earth Sciences (1987). After the training pro-

cedure, the network is capable of automatically delineate thegas pockets locations with more than 97% accuracy.

Therefore, the main contribution of this work is the creationof an innovative method for the detection of possible gas sig-natures in seismic traces. Moreover, to the best of our knowl-edge, is the first work applying the windowing technique witha LSTM neural network. Such an approach can become a pow-erful tool when it comes to probable hydrocarbon targets withnot clear seismic information, low illumination e.g., sub-saltreservoirs and reservoirs associated with volcanic intrusions.

SEISMIC DATA

The seismic data used to corroborate the proposed methodol-ogy is the public 3D-seismic survey Netherlands Offshore F3-Block, which is available at the Open Seismic Repository dGBEarth Sciences (1987), maintained by dGB Earth Sciences.The dataset consists of 384 km2 of time migrated 3D-seismicdata, with 651 inlines and 951 crosslines, a time range of 1848ms, a sampling rate of 4 ms and a bin size of 25 m. The surveyis located at the North Sea shore, at Netherlands coast. Ac-companying the original 3D-seismic the repository also pro-vides filtered versions of the data, acoustic impedance (AI)cubes, some seismic attributes already computed, four wells(F02-1, F03-1, F03-4, F06-1) with markers and geophysicallogs, along with eight corresponded seismic horizon.

For the manual localization of the gas pockets and their possi-ble indicators, we used not only the available cube, but also theother accessible information provided. Along with the originalseismic data, the impedance cubes and geophysical well logshad significant roles in the labeling for the network input. Theexpert input is essential for the appropriate training and valida-tion steps of the deep-learning algorithm and the methodologyevaluation.

However, the AI cube does not present information for the en-tire seismic record (Figure 1.1(b)), and the primary concentra-tion of gas indicators is almost restricted near the sigmoidalstrata. In this sense, the seismic images were cut to a regionof interest (ROI) (Figure 1.3), preventing possible imputationof noisy data and misleading gas labeling, therefore enhancingthe quality of the neural network training.

PROPOSED METHOD

The proposed methodology concentrates in two main steps,the windowing processing and application of the LSTM neu-ral network. The following subsections explain these steps inmore details, including the proposed network architecture. Forcompleteness, these subsections also present the classical clas-sification metrics used for evaluation.

Direct Hydrocarbon Indicators based on Long short-term memory neural network

Figure 1: ROI cutting process: 1 – Available data (a) F3 Seismic Cube (b) AI cube; 2 – Area selection and cutting; 3 – ROI cube.

Windowing Processing

This stage is applied after the traditional dataset constructionfor a neural network implementation, i.e., the random divisionof the images in training, validation, and test dataset. Oncethe dataset is defined, the seismic traces of the images are ex-tracted. The windowing processing application consist in theextraction of one-dimension patches along the seismic trace.In this process each seismic trace was extracted using fortysamples window-length with superposition one sample eachwindow. The chosen 40x1 window size allows the patches tobe large enough to carry significant information as well as in-formation from neighboring regions. The Figure 2 exemplifiesthe windowing process on a seismic trace.

Figure 2: Windowing Processing.

LSTM Neural Network

The main advantage of using an LSTM neural network is thatit allows a memory of previous inputs to persist in the net-works internal state, and thereby influence the network out-put (Goodfellow et al., 2016). Thus, previous samples of theseismic trace will affect the classification of further ones. Inthis sense, the model can identify the physical gas effect onthe seismic signal before the arrival of the gas signature on theseismic trace. Leading this way to a better classification modeltacking the DHI identification on the seismic data.

Moreover, a 1D approach has the benefit of producing a larger

quantity of samples to train and analyze the model. For thepreparation of a deep neural network, a large amount of data isusually required, and a 2D or 3D approach, using the seismicimage, could not satisfy such samples quantity.

The architecture of an LSTM model consists of a set of re-curring connected subnets called memory blocks. Each blockcontains one or more self-connected memory cells and threemultiplicative units: input, output, and forget gate ports, whichperform the write, read, and reset operations for the cells. Ingeneral, an LSTM network model is very similar to a tradi-tional recurrent model, with the exception of replacing the hid-den layers by the memory blocks.

An LSTM neural network process a given input x =( x1, x2, ... , xn−1, xn) to an output y = ( y1, y2, ... , yn−1, yn)using the following iterative equations (Sak et al., 2014):

it = σ(Wix xt + Wim mt−1 + Wicct−1 + bi) (1)

ft = σ(W f x xt + W f m mt−1 + W f cct−1 + b f ) (2)

ct = ft � ct−1 + it � g(Wcx xt + Wcmmt−1 +bc) (3)

ot = σ(Wox xt + Wom mt−1 + WocCt + bo) (4)

mt = ot � h(ct) (5)

Where, W is the weight matrices, the b terms are the bias vec-tors, σ is the logistic sigmoid function, i, f, o and c are theinput gate, forget gate, output gate and cell activation vectorsrespectively. All the gates are the same size as the cell outputactivation vector m. � is the element-wise product of the vec-tors, g and are the cell input and cell output activation func-tions respectively. Generally, and in this work, the networkactivation function (g and h) is represented by tanh(x).

Regarding the fully connected layers, after the LSTM cells,the hidden layers presents the same activation function ReLU.Whereas, the output layer use the softmax fuction. Figure 3illustrate the proposed network.

Direct Hydrocarbon Indicators based on Long short-term memory neural network

LSTM LSTM LSTMInput

Fully-connectlayers

256 Neurons256 Neurons 2 Labels

3 LSTM Cells

40 Units 40 Units 40 UnitsWindows40 x 1

p(gas)Є [0, 1]

p (nogas) = 1 -

p(gas)

Figure 3: Proposed LSTM Architecture.

Metrics

To evaluate the proposed method we used four traditional met-rics that involves classification problems: sensitivity, speci-ficity, accuracy, and area under ROC curve (AUC) (Fawcett,2006). The first one, the sensitivity, evaluates the model’sability to predict a particular interest class, whereas, the speci-ficity measures the ability of the model to accurately predictthe complementary class. As for the accuracy, that representsthe weighted average of the interest classes, and lastly, the areaunder the ROC curve (AUC) metric that represents how well isthe proposed model prediction regarding the evaluated classes.The following expressions denote the above metrics:

sensitivity =T P

T P+FN(6)

speci f icity =T N

T N +FP(7)

accuracy =T P+T N

T P+T N +FP+FN(8)

1mn

m∑i=1

n∑j=1

Γpi>p j (9)

Where, TP is the number of true positives (gas samples classi-fied as gas), TN is the number of true negatives (non-gas sam-ples classified as non-gas), FP is the number of false positives(non-gas samples classified as gas), and FN is the number offalse negatives (gas samples classified as non-gas). Regardingthe AUC expression, i runs over all m data points with truelabel 1, and j runs over all n data points with true label 0; piand p j denote the probability score assigned by the classifierto data point i and j, respectively. Γ is the indicator function:it outputs 1 if the condition (here pi > p j) is satisfied.

Through them, it is possible to compare the models generatedfrom different algorithms or the same algorithms subject to dif-ferent parameters. Thus, they are essential to finding the bestconfiguration of a classification model

RESULTS AND DISCUSSION

To evaluate the proposed algorithm we used the NetherlandsF3-Block 3D seismic data. Therefore, the dataset was com-posed of 691 seismic images, including cross-lines and in-lines, divided into three parts: training, testing and validation.For the test and validation datasets, 200 seismic images wererandomly chosen. Regarding the training dataset, 491 imageswere used.

For the windows classification we trailed the following criteria:if the window has 50% of its value as gas it is considered a gaswindow, otherwise a non-gas window. Thus, the distributionof the dataset, in relation to the number of windows, was:

Dataset Gas Non-GasTrain 1.154.386 5.159.114

Validation 237.408 9.775.092Test (Inline) 139.401 6.768.099

Test (Crossline) 55.555 3.049.445

Table 1: Windows samples per dataset.

As the original dataset is considerably unbalanced (Table 1),in other words, the number of windows containing gas areconsiderably small compared to non-gas regions, it becomesextremely difficult for the model to differentiate well the twoclasses. Thus, one-way around this problem, for the trainingdataset, was to select all traces, of a given seismic image, con-taining gas samples and randomly select 20% more traces con-taining non-gas samples for the same image. Note howeverthat for the validation and test dataset, all traces were used.

The metrics on Table 2 show the efficiency achieved for theproposed algorithm. Through them, it is possible to note thatthe high value of sensitivity and specificity, approximately95% and 97% respectively, indicate that the model succeedpredicting the gas accumulation regions with low rate of falsepositives (incorrect gas predictions by the model). Moreover,the AUC measure, with almost 99%, indicates that the pro-posed algorithm was able to differentiate both classes fromeach other.

Direct Hydrocarbon Indicators based on Long short-term memory neural network

Base MetricsAccuracy (%) Sensitivity (%) Specificity (%) AUC (%)

Inline 97.16 97.83 97.15 98.80Crossline 96.83 94.77 96.87 98.71

Table 2: Windows samples per dataset.

The Figure 4, Figure 5, Figure 6, and ?? present the proposedmethod graphical output and the respective heat map. The re-sults corroborate the metrics indication above. Almost the en-tire gas label (red marks) is predicted by the model (green la-bel), with some boundary constraints caused by the windowingoverlap. The presence of false positives, indicated by the met-rics, are mostly concentrated on the clinoform strata and theupper layers. However, the heat map, that represents the win-dow probability distribution, evidence that most of the falsepositives have low confidence of prediction. Therefore, mostof them can be disregard as possible gas-DHI.

(NEG)

(POS)

(NEG)

(POS)

0%

50%

100%

Figure 4: Model prediction result for In-Line 249.

(NEG)

(POS)

(NEG)

(POS)

0%

50%

100%

Figure 5: Model prediction result for In-Line 120.

As for the computational time spent, considering only the opti-mum network architecture, the proposed one, we used approx-imately four hours of training. The hardware configuration forthe training step consisted of one NVIDIA Titan X GPUs. Af-ter the completion of the training phase, the classification ofall the windows of a seismic image is accomplished in approx-imately ten seconds per image.

(NEG)

(POS)

(NEG)

(POS)

0%

50%

100%

Figure 6: Model prediction result for Cross-Line 846.

CONCLUSION

This work presents a novel methodology to find gas signaturesin seismic traces, yielding good DHIs on seismic images. Tovalidate the method we used a real open seismic dataset to de-tect the gas regions. Quantitative metrics and heat maps con-firm the quality of the results.

The method uses the seismic traces, a 1D approach, instead ofthe seismic image, 2D or 3D approach. With this 1D strategy,we were able to produce more labeled samples with the sameamount of interpreted data. Moreover, the LSTM applicationon the seismic trace gave the advantage of performing the anal-ysis always with previous knowledge of prior examined sam-ples. Therefore, it was possible for the model to understandpossible gas effects on the seismic traces before the actual gasaccumulation is analyzed.

The method proved to be robust, with considerably high met-rics and solid graphical results. Thus, it is possible to concludethat the proposed method can effectively assist the experts dur-ing the task of detecting possible DHI signatures on seismicimages. Hence, optimizing the seismic interpretation proce-dure, reducing costs and time of an O&G exploratory phase.

ACKNOWLEDGMENTS

The authors would like to thank dGB Earth Sciences for main-taining and managing the Open Seismic Repository at Ter-raNubis, the Tecgraf PUC-RIO institute for providing the nec-essary funding and infrastructure to conduct this research. Theauthors also would to thank the CAPES and CNPQ organiza-tions for also providing funding for the development of thisresearch.

Direct Hydrocarbon Indicators based on Long short-term memory neural network

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