Dipole Shear Sonic Imager Research Articles

Determining the geomechanical units using rock physics methods

The stability of the drilled wellbores in carbonate formations is of great importance. This paper is a new approach to determine the elastic properties of the wellbore based on rock physics methods using the Hashin-Shtrikman bounds to determine the wellbore stability. Initially, the static elastic parameters were determined by two different methods, namely the rock physics method and DSI log, followed by clustering through Multi-Resolutional Graph-Based clustering (MRGC) and calculating geomechanical-units (GMUs) of each method separately. The obtained results from DSI log and rock physic methods were then compared followed by determining the wellbore stability. The results showed that the correlation between shear and compressional wave velocity obtained from the petrophysical method with the measured values of shear and pressure wave velocity in the well was 0.94 and 0.90, respectively, which shows that the petrophysical method can estimate these two logs with high accuracy, and it can be a suitable alternative instead of using the Dipole Shear Sonic Imager (DSI). It was observed that the geomechanical units of elastic parameters calculated by proposed rock physics method are in good agreement with those obtained from DSI log. Rock-physic method can be a good alternative for when expensive DSI logs are missing.

Comparing Artificial Intelligence Algorithms with Empirical Correlations in Shear Wave Velocity Prediction

Accurate estimation of shear wave velocity (Vs) is crucial for modeling hydrocarbon reservoirs. The Vs values can be directly measured using the Dipole Shear Sonic Imager data; however, it is very expensive and requires specific technical considerations. To address this issue, researchers have developed different methods for Vs prediction in underground rocks and soils. In this study, the well logging data of a wellbore in the Iranian Aboozar limestone oilfield were used for Vs estimation. The Vs values were estimated using five available empirical correlations, linear regression technique, and two machine learning algorithms including multivariate linear regression and gene expression programming. Those values were compared with the real Vs data. Furthermore, three statistical indices including correlation coefficient (R2), root mean square error (RMSE), and mean absolute error (MAE) were used to evaluate the effectiveness of the applied techniques. The mathematical correlation obtained by the GEP algorithm delivered the most accurate Vs values with R2 = 0.972, RMSE = 0.000290, and MAE = 0.000208. Compared to the available empirical correlations, the obtained correlation from the GEP approach uses multiple parameters to estimate the Vs, thereby leading to more precise predictions. The new correlation can be used to estimate the Vs values in the Aboozar oilfield and other geologically similar reservoirs.

Robust estimation of shear wave velocity in a carbonate oil reservoir from conventional well logging data using machine learning algorithms

In recent decades, various applications of shear wave velocity have been reported in oil and gas projects. These applications include the determination of lithology, estimation of geomechanical parameters, reservoir fluid detection, etc. The shear wave velocity is usually measured or recorded by DSI (Dipole Shear Sonic imager) instruments. This log is not usually available in all wells of a hydrocarbon field because of the high cost of running this log compared to other conventional logs. However, it is tried to estimate the shear wave velocity from other related logs.The literature contains many empirical rock physics models used to estimate shear wave velocity. Most of them were developed for sandstone lithologies and didn't applicable in carbonate reservoir because of existing some vertical and horizontally complex fracture networks in the carbonate reservoirs that cause a quite different behavior for the shear wave velocity.The main objective of this research is to concentrate on the effect of lithology in the estimation of the target log for a carbonate reservoir. At the earliest step, preprocessing is performed and 455 data points of several conventional well logs, i.g., acoustic, density, neutron porosity, resistivity, gamma-ray, calcite volume, dolomite volume, and water saturation logs have been applied to generate the synthetic shear wave velocity log in a carbonate oil reservoir.First, original data are divided into training, validation, and testing subsets. Different data-driven predictive models were built: Multiple Linear Regression (MLR), Ensemble Learning Method (ELM), and three architectures of Artificial Neural Networks (ANN). The performance of all techniques was compared with each other. Finally, the feed-forward neural network shows the highest accuracy with R-values of 0.99 and 0.96 for the training and testing datasets. Moreover, the network design and tuning parameters of the model are adjusted using the grid search optimization technique to obtain the optimal design of the artificial neural network for estimating the shear wave velocity log. Hence, a deep artificial neural network, i.e., [8–11–15-1] network with bayesian regularization training algorithm and hyperbolic tangent sigmoid transfer function, is proposed to predict the target response in other wells.

Heterogeneity evaluation of pore types based on dipole shear sonic imager logs by means of statistical parameters, the central Persian Gulf

AbstractHeterogeneity analysis in carbonate reservoirs is of great importance for the interpretation of their characteristics. This study focuses on the evaluation of pore‐type heterogeneity by means of the sonic wave velocities in the carbonate–evaporite series of Permian–Triassic sequences in the Persian Gulf. Thin section petrography and laboratory measured the porosity and permeability of 1576 samples, as well as routine wire‐line logs, were employed. To manage heterogeneity, six electrofacies (EF) were determined based on an integrated analysis of compressional, shear and Stoneley waves as well as three routine logs (gamma, neutron and density). The frequency of pore types in each electrofacies was calculated. Then, the heterogeneity of pore‐type distribution in each electrofacies was measured by standard error and coefficient of variation. Sonic velocity has been compared with the geological properties of electrofacies. Results indicated that velocity is affected by factors other than the amount of porosity, facies or lithology. The standard error values in all electrofacies were close to zero. The coefficient of variation values ranges from 1.08 to 4.60. The lowest amount of coefficient of variation was observed in EF6 in the Khuff (K) 4 zone for moldic porosity. This value for intercrystalline porosity distribution in EF1 in the K1 zone was low, too. These results indicate the lowest standard deviation and the highest homogeneity in the distribution of intercrystalline and moldic porosities in K1 and K4. Also, comparing the mean values of sonic logs in each electrofacies with the total mean value in the whole sequence indicated that there is a relationship between sonic velocity logs and the type of porosity. High values of sonic velocity correspond to intercrystalline porosity in K1 and low values of velocity correspond to moldic porosity in K4. Results show that integrated pore‐type studies should be carried out to understand the sonic velocities in carbonate reservoir rocks.

Predicting dynamic shear wave slowness from well logs using machine learning methods in the Mishrif Reservoir, Iraq

Shear wave slowness is needed in reservoir characterization for seismic modeling, amplitude variation analysis and determination of rock elastic properties. Conventional methods such as multi-array polarization acoustic measurements as well as Dipole Shear Sonic Imager DSI are employed for measuring the shear slowness directly. However, data from these tools aren't available in all wells, particularly older ones, and in some cases, due to high cost and technical difficulties (for instance, in cased hole). Consequently, statistical techniques and machine learning applications are quick and can be applied with the appropriate algorithm to obtain this acoustic parameter. This provides reasonable results, saves time and cost of running experiments and well loggings. This research, therefore, provides an improved methodology via Geolog software's Facimage module to predict the dynamic shear slowness for the Mishrif formation, Iraq. Five different techniques were employed: self-organizing map (SOM), ascendant hierarchical clustering (AHC), dynamic clustering (DYN), artificial neural networks (ANNs), and multi-resolution graph-based clustering (MRGC). The predictions yielded root mean square error (RMSE) of 5.778, 6.914, 6.011, 4.845 and 2.897, while the R – squared (R2) values are 0.838, 0.766, 0.824, 0.888, and 0.9591 for SOM, AHC, DYN, ANN and MRGC methods, respectively. Implying that the MRGC yielded a predicted shear wave slowness with best match. The application of this advanced statistical approach in reservoir characterization will help to exploit the huge well log data available, consequently, saving time for reservoir description and production cost as well as improved ultimate recovery.

The effect of frequency bandwidth on DSI anisotropy evaluation

Anisotropy evaluation in carbonate rocks plays a pivotal role in their reservoir characterization and modeling. This study demonstrates the benefits of advanced interpretation using the Dipole Shear Sonic Imager (DSI) log data for anisotropy evaluations of porous media. Splitting the shear wave into fast and slow components, with different polarizations and velocities, provides useful indications of formation anisotropy. Fast shear waves travel faster in directions parallel to layering and fracturing while slow shear waves travel more slowly perpendicular to layering and fracture networks. Quantifying the difference between fast and slow shear velocities provides a measure of acoustic anisotropy in the medium penetrated.The purpose of the research is to evaluate the effects of frequency bandwidth on the depth of the wave propagation rates and slowness values for anisotropy analysis, thus four bandwidth frequencies: 1–2 kHz, 2–4 kHz, 4–6 kHz, and 1–5 kHz are selected and examined in a vertical drilled well in the gas reservoirs of Upper Dalan Member, and Kangan Formations in the South Pars Gas-Condensate Field.The frequency filtering, semblance analysis, and slowness picking are done on waveforms for frequency-slowness dispersion analysis and slowness correction. The dispersion correction for bandwidth 4–6 kHz isn't performed completely in good hole condition intervals, because high- frequencies around 6 kHz are absorption due to the intrinsic attenuation of the rocks. Therefore, the considered bandwidth is decreased to 4–5.5 kHz. In the following, Alford rotation, align waveforms, and calculation and evaluation of derived fast and slow components are done for deriving the azimuth of the fast shear wave for each bandwidth of waveforms. The dominant trend of the azimuth of anisotropy is that aligned with the azimuth of the fast shear wave.The azimuth of the fast shear wave as azimuth of anisotropy and its corresponding intervals are compared with the results of image log data for the same well interval. This made it possible to reject or confirm the findings of the DSI acoustic anisotropy analysis for each bandwidth considered.In the low bandwidth frequency (1–2 kHz), the differences in slowness, energy, travel time, and penetration depth are more extreme than those recorded by the other bandwidths considered. The azimuth of anisotropy is NW-SE with a slight E-W trend. However, the azimuth of anisotropy was found to be contrary to the image log interpretation where the dominant trend is NE-SW.The dominant trend of the azimuth of anisotropy for bandwidth 2–4 kHz is NE-SW with a slight E-W trend. The anisotropy directions and zones are consistent with those identified from the wellbore image log results. Moreover, the penetration depth is meaningful and far away from the borehole drilling and mud effects. These results suggest that the bandwidth (2–4 kHz) is best suited for acoustic anisotropy analysis of wellbore sections using Dipole Shear Sonic Imager (DSI) recorded data in this well.The trends of the azimuth of anisotropy for bandwidth 4–5.5 kHz are NE-SW, and NW-SE, with expanded tolerance azimuth of anisotropy. The results that have been achieved in a small penetration depth and close to the borehole wall are affected by signal absorption and attenuation in flushed and transition areas. This leads to some errors and inconsistencies with the image log interpretation.The trends of the azimuth of anisotropy for bandwidth 1–5 kHz are NE-SW, NW-SE, and E-W. The outcomes are the consequence of the variety of directions and zones associated with this wider bandwidth, together with the penetration depths of the waveforms in the diverse zones considered. However, the azimuth of anisotropy is inconsistent with the image log interpretation.

New Equations to Determine Shear Velocity from Compressional Velocity in Hf-2 Well, Halfaya Oil Field

The most important characters of the rock formations are shear and compression velocities, which are used to calculate elastic moduli. The geomechanical properties can be used to evaluate reservoir stability or to calibrate velocity and time/depth conversion, synthetic seismogram and other important applications. The aim of this study is to use the first order least square method to obtain an empirical equation to determine the shear velocity from compressional velocity for Mishrif, Rumaila, Ahmadi, and Mauddud formations in Hf-2 well. The obtained equations depend on sonic log and dipole shear sonic imager data of Hf-2 well and use Techlog software. The results from the obtained equations in comparison with those results of Green Castagna equation and measured data of dipole shear sonic imager of shear velocity for Mishrif, Rumiala and Mauddud formations. It is found from the comparison that the result is consistent each other’s. The consistent reflect the reality of shear waves values obtained in these formations. The comparison in Ahmadi formation also shows a consistent result for the obtained equations but it shows some differences from the dipole shear sonic imager and shear velocity reference values. The authors believed that the difference in some values of the Ahmadi Formation may be due to the high shale volume in comparison to the other formations.

Pore Pressure Prediction and Its Relationship with Rock Strength Parameters and Weight on Bit in Carbonate Reservoirs (A Case Study, South Pars Gas Field)

Accurate pore pressure knowledge is particularly critical in drilling, such as drilling safety (avoidance of blowouts and well control lost-time incidents). Pore pressure prediction in carbonate reservoir due to the fact that the pore system and fractures are more complex than the clastic reservoir, also the effectiveness of pore pressure on drilling parameters like weight on bit that has direct effects on drilling rate is very important. As well as, the pore pressure and rock strength parameters have a special place in geomechanical studies. In this study, pore pressure was calculated using the porosity and effective stress in the carbonate reservoir. The porosity was calculated with three methods: neutron–density, neutron–sonic and sonic. The rock strength parameters (Bulk and Shear modulus) were calculated using P and S waves obtained from dipole shear sonic imager log. The calculated pore pressure was compared with modular formation dynamics tester (MDT), WOB and rock strength parameters. The result of the study showed that among available methods, predicted pore pressure with sonic porosity has the best matched to MDT data. Moreover, pore pressure has an inverse relationship to WOB and rock strength parameters, so that with increasing pore pressure of both WOB and rock strength parameters were decreasing. The comparison among predicted pore pressure, mud weight and MDT data was showing that the predicted pore pressure has high accuracy and can use it for geomechanical studies and having a safe drilling operation.

DISCRETE FRACTURE NETWORK (DFN) MODELLING OF FRACTURED RESERVOIR BASED ON GEOMECHANICAL RESTORATION, A CASE STUDY IN THE SOUTH OF IRAN

Fractured reservoirs have always been of interest to many researchers because of their complexities and importance in the oil industry. The purpose of the current study is to model the fractured reservoir based on geomechanical restoration. Our target is the Arab Formation reservoir which is composed of seven limestone and dolomite layers, separated by thin anhydrite evaporate rock. First of all, in addition to the intensity, the dip, and the azimuth of the fractures, the magnitude and the direction of the stresses are determined using wireline data e.g. photoelectric absorption factor (PEF), sonic density, neutron porosity, a dipole shear sonic imager (DSI), a formation micro imager (FMI), and a modular formation dynamics tester (MDT). Then, the seismic data are interpreted and the appropriate seismic attributes are selected. One of our extracted attributes was the ant tracking attribute which is used for identifying large-scale fractures. Using this data, fractures and faults can be identified in the areas away from wells in different scales. Subsequently, the initial model of the reservoir is reconstructed. After that, the stress field and the distribution of fractures are obtained using the relationship between the stresses, the strains, and the elastic properties of the existing rocks. The model is finely approved using the azimuth and the intensity of fractures in the test well. Our findings showed that the discrete fracture network (DFN) model using geomechanical restoration was positively correlated with real reservoir conditions. Also, the spatial distribution of fractures was improved in comparison to the deterministic-stochastic DFN.

Improved Estimation of Shear-Wave Velocity by Ordered Weighted Averaging of Rock Physics Models in a Carbonate Reservoir

The aim of this paper is to improve estimation of shear-wave velocity in carbonate rocks. The region being studied is an oil field located in southwest Iran, where Upper Cretaceous carbonates act as the producing reservoir. These fracture bearing carbonates make the study of this reservoir complicated. Moreover, the accuracy and the precision of microstructure and pore type can affect the rock physics models by either directing or distracting these models from the typical models. The geological studies (thin sections, scanning electron microscope and core computer tomography scan) show that the reservoir mainly contains large pores with aspect ratios between 0.1 and 0.9. The existence of fractures is also demonstrated by a formation micro-imager and geological analyses. In this study, the applications of two rock physics models are investigated: (1) self-consistent approximation following the Gassmann equation and (2) Xu–Payne method, which has been carried out using the differential effective medium approach. In addition, for improving model predictions, these two rock physics models are fused by the ordered weighted averaging (OWA) aggregation operator that offers the most appropriate results. Furthermore, there are similarities between the three mentioned models based on the estimated values when compared to the measured dipole shear sonic imager log. Ultimately, the analysis and results demonstrate that the OWA model gives the best compatibility with the original well-log data. In other words, the reason why the OWA model provides the best results in the studied carbonates can be related to its optimization algorithm for defining model parameters.

Using drilling data to compare geomechanical parameters with porosity (a case study, South Pars gas field, south of Iran)

As regards to deficiency or lack of core data and high cost of coring, it was seen necessary to utilize drilling data to study and compare calculated dynamic parameters by log, instead of core data. The elastic modulus of rock includes E (Young’s modulus), ν (Poisson’s ratio), K (bulk modulus), and G (shear modulus) which are essential geomechanical parameters which are widely used for analysis wellbore stability. In this study, due to the unavailability of the core data, the weight on bit (WOB) was used to compare the rock strength parameters instead of core data. These parameters were calculated by a DSI (dipole shear sonic imager) tool, RHOB, and NPHI log for the Dalan Formation. As regards, the amount of rock porosity is a direct relationship with rock density, and both parameters are effective on wellbore stability and ROP, the relationship between porosity and rock strength parameters was studied. As regards, the amount of rock strength is effective on ROP and WOB, and according to the availability of WOB data, the calculated parameters were compared with WOB. The results indicated that the value of these parameters has an inverse relationship with the amount of porosity. By investigating the relationship between porosity and elastic modulus, it was found that the elastic modulus decreases with increasing porosity. The comparison of porosity and elastic modules denoted that among elastic modulus, bulk modulus has the highest correlation with porosity, and the Poisson ratio has the least correlation with porosity. The UCS (unconfined compressive stress) calculated with the static Young’s modulus showed the best correlation with porosity than the calculated UCS. An essential part of the drilling process is adding force to the drill bit in order to successfully break the rock. Weight on bit is the amount of downward force exerted on the drill bit. The amount of WOB is depending on the rock strength, which is the minimum stress required to fail the rock formation. Comparison of calculating the elastic modulus and UCS with weight on bit illustrated that the calculated UCS with Es has the best correlation with weight on bit. By comparing the calculated elastic modulus with the weight on bit (WOB), it was found that these parameters are in a good matching with the parameter obtained through drilling and so it can be applied to assess the accuracy of the calculated elastic modulus parameters.

Characterization of micro-fractures in carbonate Sarvak reservoir, using petrophysical and geological data, SW Iran

In this paper, an integrated study of micro-fractures is carried out in Sarvak Formation. To achieve this goal the data from three wells (A, B and C) in one of the southwestern Iranian oil fields were used. In this study petrographic thin sections, porosity-permeability core data, image logs, Dipole Shear Sonic Imager (DSI) log, Velocity Deviation Log (VDL), Hydraulic Flow Units (HFU) concept, resistivity logs and drilling mud reports were used to identify and analyze micro-fractures in Sarvak Formation. The micro-fractures were first detected in thin sections and image logs directly, then the results of other methods in micro-fracture zones were investigated. The results show there is an abnormal trend of porosity-permeability in core data which is compatible with micro-fracture zones. Also, the relationship between micro-fractures and flow units was studied. Eight HFUs were recognized using porosity-permeability core data and FZI* (Flow Zone Indicator-Star) method. Later each of the HFUs was divided into two sub-HFUs; one of them is well-matched with the micro-fracture zones. The negative parts of VDL were in good agreement with the micro-fracture zones. The slowness anisotropy was mainly affected by the micro-fracture zones, but the energy and traveltime anisotropies were under the influence of the other phenomena such as solution seams, shale layers and borehole washouts. The fast shear wave azimuth was mainly aligned with the SHmax (maximum horizontal stress) direction, however, in micro-fracture zones, the direction suddenly changes. It was also observed that the micro-fractures do not have a considerable effect on Stoneley wave reflection, while the borehole washouts and shale layers have a great impact on this. The mud loss and mud invasion were very low in micro-fractured zones, which is related to the lack of lateral continuity of micro-fractures in the formation. The measured fracture apertures using thin section and image logs are often less than 0.1 mm, but the calculated values using Pezard-Anderson equations are very high.

Estimation of DSI log parameters from conventional well log data using a hybrid particle swarm optimization–adaptive neuro-fuzzy inference system

Dipole shear sonic imager (DSI) log offers valuable data for geophysical, petrophysical and geomechanical studies of the hydrocarbon bearing intervals. Compressional (Vp), shear (Vs) and Stoneley (Vst) waves are acquired through processing and interpreting the DSI data. The present study aims to present an improved approach for establishing a quantitative relationship between conventional well log data and sonic wave velocities (Vp, Vs and Vst as DSI log parameters) by coupling an evolutionary computational method based on a neuro-fuzzy inference system. Particle swarm optimization – neuro fuzzy inference system (PSO-ANFIS) model suggested in this study, is based on a combination of fuzzy rule-based system and particle swarm optimization algorithm, which simultaneously adjust both the antecedent and the consequent variables. This approach was conducted in siliciclastic/carbonate Asmari Formation of Mansuri oilfield in order to estimate the compressional, shear and Stoneley wave velocities. The conventional wireline logs from two wells were employed to build intelligent models while the third well was applied to evaluate the performance and reliability of the created models. The results indicated that the proposed hybrid scheme can satisfactorily improve the computational efficiency and performance of the DSI log parameter prediction, compared to individual intelligent systems and hybrid particle swarm optimization –neural network strategy (PSO–ANN). Finally, the workflow outlined here can be used as an efficient tool for prediction of other petrophysical rock properties, due to an enhanced estimation accuracy afforded by PSO-ANFIS model.

A new approach to determine geomechanical parameters of Vertical Transverse Isotropic media using VSP data

Conventionally, high frequency Dipole Shear sonic Imager (DSI) logs are used for anisotropic modeling where fast and slow shear wave's velocities are required. However, the results obtained from a DSI log are restricted to a specific and possibly short interval of the wellbore. The aims of this paper are to use Vertical Seismic Profile (VSP) data and show its application in geomechanical analysis of subsurface layers under anisotropic condition. After processing and separating upgoing and downgoing P- and S-waves, a methodology based Vertical Transverse Isotropic (VTI) condition was presented to determine elastic stiffness parameters. Having stiffness parameters determined, elastic modulus, strength and in-situ stress parameters were estimated and calibrated against the field and core sample data. Although the VSP based geomechanical parameters were calibrated against the real field data, the accuracy of the method cannot be as much as that of the well logs. However, the method presented in this paper may become a very good asset for geomechanical evaluation of the intervals where well log data are not available.

A Fuzzy Logic Model for Predicting Dipole Shear Sonic Imager Parameters From Conventional Well Logs

Sonic wave velocities, including compressional, shear, and Stoneley, are functional and practical parameters for various branches of reservoir characterization. In the last few years, several tools such as the dipole shear sonic imager (DSI) were introduced for measuring sonic wave velocities. Most of these instruments are expensive and drilling companies do not run them in all wells of a field because of economical restrictions. In this study, an accurate, intelligent, and indirect method was presented for prediction of sonic wave velocities, which are directly obtained from dipole shear sonic log, utilizing conventional well log data and fuzzy logic technique. Furthermore, the proposed methodology was applied on a carbonate reservoir of Iran. The results of the case study demonstrated the capabilities of fuzzy logic for estimation of sonic wave velocities, where DSI may not be run. The MSEs of the predicted Vp, Vs , and Vst in the test data calculated 1.47 (US/F), 4.96 (US/F), and 2.219 (US/F), respectively, which correspond to the R 2 values of 91.8%, 89.3%, and 90.4%, respectively.

Core-log integration studies in hole-A of Taiwan Chelungpu-fault Drilling Project

SUMMARY Taiwan Chelungpu-fault Drilling Project (TCDP) was initiated to understand the physical mechanisms involved in the large displacements of the 1999 Taiwan Chi-Chi earthquake. Continuous measurements of cores (including laboratory work) and a suite of geophysical downhole logs, including P- and S-wave sonic velocity, gamma ray, electrical resistivity, density, temperature, electrical borehole images and dipole-shear sonic imager, were acquired in Hole-A over the depth of 500–2003 m. Integrated studies of cores and logs facilitate qualitative and quantitative comparison of subsurface structures and physical properties of rocks. A total of 10 subunits were divided on the basis of geophysical characteristics. Generally, formation velocity and temperature increase with depth as a result of the overburden and thermal gradient, respectively. Gamma ray, resistivity, formation density, shear velocity anisotropy and density-derived porosity are primarily dependent on the lithology. Zones with changes of percentage of shear wave anisotropy and the fast shear polarization azimuth deduced from Dipole Shear-Imager (DSI) are associated with the appearance of fractures, steep bedding and shear zones. The fast shear wave azimuth is in good agreement with overall dip of the bedding (approximately 30° towards SE) and maximum horizontal compressional direction, particularly in the Kueichulin Formation showing strong shear wave velocity anisotropy. Bedding-parallel fractures are prevalent within cores, whereas minor sets of high-angle, NNW–SSE trending with N- and S-dipping fractures are sporadically distributed. The fault zone at depth 1111 m (FZA1111) is the Chi-Chi earthquake slip zone and could be a fluid conduit after the earthquake. The drastic change in fast shear wave polarization direction across the underlying, non-active Sanyi thrust at depth 1710 m reflects changes in stratigraphy, physical properties and structural geometry.