Sonic Transit Time Research Articles

Predicating of Formation Pore Pressures in Tertiary Reservoirs Using Geophysical Wireline Log Data

Changes to certain geophysical characteristics, such as sonic transit time with depth, indicate the pressure system of over-pressured zones. In this study, well-log data is used to investigate the distribution of formation overpressures in the tertiary reservoirs of one Iraqi oil field. Three wells located within the tertiary carbonate reservoir of one Iraqi oil field are of interest in this study. To assess the reservoirs, a petrophysics log analysis was carried out. The reservoir's intervals are the area of interest. The sonic velocity data was used to create a normal compaction trend line, and the overpressure zones were identified by observing the reversals in the normal compaction trend line. The pore pressure, fracture pressure, and gradients were calculated using the Eaton method. The method relies on the correlation between the normal transit time found on the normal compaction trend and the observed transit time from the log reading. The sonic log determines the bulk density and matches the density derived from the density log. Abnormal pressures were identified primarily within the area of interest, especially with unit B in Well 1. In Well 2, the abnormal pressures were recognized in zones above the area of interest. Abnormal pressures in Well 3 were identified primarily within all areas of interest. The importance of this work is to help predict overpressure zones in the research area before drilling or workover.

Predicting missing sonic logs with seismic constraint

Compressional and shear sonic transit time logs (DT and DTS, respectively) provide important petrophysical and geomechanical information for subsurface characterization. However, they are often not acquired in all wells because of cost limitations or borehole problems. We develop a method to estimate the DT and DTS simultaneously from other commonly acquired well logs such as gamma ray, density, and neutron porosity. Our method consists of two consecutive models to predict the sonic logs and the seismic traces at the well locations. The model predicting the seismic traces adds a spatial constraint to the model predicting the sonic logs. Our method also quantifies the uncertainties of the prediction, which come from the uncertainties of neural network parameters and input data. We train the network on four wells from the Poseidon data set located on the Australian shelf in the Browse Basin. We test the network on the other two wells from the Browse Basin. The test results indicate better predictions of sonic logs when we add the seismic constraint.

Geochemical evaluation and source rock zonation by multi-layer perceptron neural network technique: a case study for Pabdeh and Gurpi Formations-North Dezful Embayment (SW Iran)

In this study, using a multi-layer perceptron neural network (MLPNN) model, total organic carbon (TOC) and hydrogen index (HI) values for Pabdeh and Gurpi Formations in the oil fields of Naft Sefid (NS-13), Kupal (KL-36, KL-38, and KL-48) and Palangan (PL-2) were calculated in the North Dezful Embayment located in the southwest of Iran. To build the MLPNN model, the geochemical data calculated by the Rock–Eval pyrolysis method (TOC and HI) and the conventional petrophysical well log data, including sonic transit time log (DT), formation density log (RHOB), total resistivity log (RT), spectral gamma-ray log, computed gamma-ray log and neutron porosity log from the NS-13 well were used. The log data were the input layer, and the geochemical data were the output layer of the model. Twenty-four datasets were used for MLPNN training, and seven datasets were used for MLPNN testing. Two hidden layers were considered in this technique. Each hidden layer has an activation function (tanh) and a solver parameter (lbfgs). The accuracy of measurement of TOC and HI indices of Pabdeh and Gurpi Formations in terms of R2 was 0.93 and 0.90, respectively. This model has higher accuracy than the ΔlogR technique (R2: 0.28). Considering the relationships between the input data and other wireline logs is an advantage of this technique. These two formations have five source rock zones. Pabdeh Formation has three zones. The middle zone of the Pabdeh Formation (Pz. II) has the highest TOC (2.6 wt%) and source rock potential. Pabdeh Formation has kerogen type II. Gurpi Formation has a weaker source rock potential than Pabdeh Formation due to its low TOC content (< 1%). Both source rock zones of this formation have low TOC, but in some layers of the lower zone of the Gurpi Formation (Gz. II), high values for TOC were predicted. Gurpi Formation has Kerogen types II and III.

Comparison of measured versus modeled TOC in the Tuscaloosa marine shale of Southwestern Mississippi, U.S.A.

This study presents a comparison of measured versus modeled total organic carbon (TOC) in the Upper Cretaceous Tuscaloosa marine shale (TMS) of southwestern Mississippi as a case study to evaluate the effects of mineralogy on the TOC estimated from the ΔlogR method. The ΔlogR method is utilized to calculate TOC, which involves baselining sonic transit time and resistivity log curves in a non-source rock section of the formation. In our application, the well log curves were baselined in the upper TMS, which is described as a non-source rock section, and in the lower TMS above the high resistivity zone (HRZ), which is described as having a higher carbonate content. The ΔlogR calculated TOC values from these two baselining approaches show that the lower baseline results in improved agreement between measured TOC and calculated TOC. This improvement is likely to be due to the lower baseline accounting for the increase in resistivity caused by higher carbonate content, in addition to any presence of TOC. The upper baseline, which has a lower carbonate content, does not account for this resistivity increase. Additionally, sample type appears to affect the comparison of measured and ΔlogR calculated TOC. Most of the samples used in this study are legacy cuttings that were not preserved during storage, exposing the high surface area cuttings to increased rates of oxidation, whereas geophysical logs record the rock properties in situ. To account for this oxidation effect, the difference between the medians of the TMS HRZ TOC core and cuttings values was added to each TOC measurement in this study, resulting in a median measured TOC value that is similar to the median of the lower TMS-baselined ΔlogR calculated TOC value. Overall, this study demonstrates that carbonate content and sample type can affect how well measured and ΔlogR-modeled TOC values compare.

Machine learning application for prediction of sonic wave transit time - A case of Niger Delta basin

Sonic wave transit times are the major logs for estimating pertinent geomechanical parameters including overburden stress, pore pressure, effective stress, and unconfined compressible strength among others. These logs contain much important information about subsurface formation. However, in most oil and gas exploration wells, these logs are not usually acquired in all the wells which can be attributed to the high cost involved in logging. The application of the data-driven ML model has rarely been used for compressional and shear sonic logs (DTP and DTS) prediction within the Niger Delta basin where many reports of geomechanical instabilities have been reported. In this study, seven machine learning algorithms namely linear regression (LR), K-nearest neighbour (KNN), support vector regressor (SVR), random forest regressor (RFR), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and back propagation artificial neural network (BPANN) were employed for prediction of DTP and DTS from conventional wireline logs data and their results were compared. For DTP prediction, the best model achieved a coefficient of determination (R2) value of 90 % using CatBoost. Also, CatBoost produced the model for DTS prediction with an R2 value of 95 %. A comparison results of the seven ML algorithms showed that six models performed really well for DTP and DTS predictions with a value of R2 > 80 % and 90 % respectively. In contrast, the worst performance models are LR for DTP prediction and SVR for DTS prediction. The blind testing carried out on some selected wells affirmed the efficiency of the proposed technique producing R2 values ranging from 92 to 94 % for DTP prediction and 94–99 % for DTS prediction. This present study confirmed the ability of building efficient ML models for estimating and predicting DTP and DTS which can be used to estimate geomechanical parameters in wells where both are not available.

Application of GMDH model to predict pore pressure

Pore pressure (PP) is one of the essential and very critical parameters in the oil and gas industry, especially in reservoir engineering, exploitation, and production. Forecasting this valuable parameter can prevent huge costs incurred by the oil and gas industry. This research aims to develop a algorithm to better predict PP in subsurface -formations. Based on this, information from three wells (F1, F2, and F3) representing one of the Middle East oil fields was used in this research. The input variables used in this research include; laterolog (LLS), photoelectric index (PEF), compressional wave velocity (Vp), porosity (NPHI), gamma ray (spectral) (SGR), density (RHOB), gamma ray (corrected) (CGR), shear wave velocity (Vs), caliper (CALI), resistivity (ILD), and sonic transit time (DT). Based on the results presented in the heat map (Spearman’s correlation), it can be concluded that the pairs of parameters RHOB-PEF, CGR-SGR, RHOB-CALL, DT-PEF, PP-RHOB, Vs-RHOB, ILD-LLS, DT-CGR, and DT-NPHI are connected. In this research the GS-GMDH methods is used for modeling which is based on the Group method of data handling (GMDH). The results of this research show that this algorithm has an average error of RMSE = 1.88 Psi and R2 = 0.9997, indicating its high-performance accuracy. The difference between this method and the conventional GMDH method is that it can use three or more variables instead of two, which can improve prediction accuracy. Furthermore, by using the input of each neuron layer, the proposed model can communicate with other adjacent and non-adjacent layers to solve complex problems in the simplest possible way.

An Improved Computational Learning-Based Model for Estimating Total Organic Carbon in Unconventional Shale Reservoirs

Unconventional resources have emerged as one of the crucial alternatives to the rapidly depleting of conventional hydrocarbon resources. The hydrocarbon potential of shale source rocks is assessed by the percentage of the organic index such as total organic carbon (TOC). Correct estimation of TOC is very important since minor deviations in anticipated results can lead to wastage of investments and time. A slight improvement in estimation practices, on the other hand, can increase the value of an exploration project. Therefore, the objective of this study is to present an improved classification and regression tree (CART) computational learning-based model as an improved alternative in estimating TOC from well logging data. Conventional well logs suite of bulk density, gamma-ray, deep resistivity, sonic transit time, spontaneous potential, and neutron porosity from Mihambia, Mbuo and, Nondwa, Formations of the Mandawa Basin Tanzania, were used as input variables. Results from the developed CART TOC model were compared with the random forest (RF) and backpropagation neural network (BPNN). It was observed that the proposed CART model trained better while generalizing better through unused testing data compared with RF and BPNN. CART model achieved R, RMSE, and MAPE values of 0.9615, 0.0840, and 0.5035 for training and 0.9703, 0.1162, and 0.3722 for testing respectively. The proposed model work with higher accuracy with the sensitivity analysis indicating that gamma-ray, deep resistivity, and sonic transit time significantly influenced the model outcome.

Augmenting and eliminating the use of sonic logs using artificial intelligence: A comparative evaluation

ABSTRACTIn oil and gas exploration, it is vital to acquire information about the bottom hole conditions. This is done in the field using wireline logging. The sonic log is one of the most prolific logs as it assists in porosity determination, cement evaluation and identification of lithology and gas‐bearing intervals. However, sonic logging tools are not always a part of the wireline logging arrangement. Still, there are sections where the logging data are missing, and in some cases, these are dependent upon old tools. The tool is incapable of recording shear wave transit times. This study explores the possibility of substituting the sonic log using machine learning and artificial intelligence techniques. These techniques can also predict the sonic log data in sections where these are missing or unreliable. Artificial neural networks, decision tree regression, random forest regression, support vector regression and extreme gradient boosting are the most popular tools available at our disposal for making these estimations. This study has compared these different techniques for their effectiveness and accuracy in making sonic transit time predictions based on other available well logs. The obtained results suggest that despite all the attention on artificial neural networks, eXtreme gradient boosting and the random forest regression outperform it for the given purpose. In the case of missing shear transit time data, random forest regression made predictions with a root mean squared error of 1.03 × 10–4 and a root mean squared error of 0.97 while eXtreme gradient boosting regression did so with a root mean squared error of 1.36 × 10–4 and the same regression coefficient (0.97). When no sonic data were available, random forest regression estimated shear transit time with a root mean squared error of 6.41×10–4 and a regression coefficient of 0.95, and compressional transit time with a root mean squared error of 9.06×10–5 and a root mean squared error of 0.94. The root mean squared error of shear transit time and compressional transit time predictions made using eXtreme gradient boosting were found to be the same as those of random forest regression. The root mean squared error, however, was observed to be slightly less for the compressional transit time predictions and somewhat more for the shear transit time predictions. As data analysis, in general, is a better method for estimation than the use of empirical correlations, these machine learning‐based predictions can serve as powerful tools in the oil and gas exploration industry.

THE EFFECT OF OIL CONTENT ON SONIC WAVE PROPAGATION (Analyses from Well Log Data)

The effect of fluid content to the sonic wave propagation in the reservoir rock hasbeen studied experimentally using the well log data. Evidence shows that the water contentaffects the sonic wave propagation only in a specific interval, while oil content also affectsthe sonic wave propagation but at a specific low interval. There is a discontinuity wherethe effect of oil content changes suddenly with an increasing oil content. Higher than thisvalue it seems that there is no effect of oil content on sonic wave transit time.

Seismic Versus Sonic Revisited

The sonic log enables all features along the depth scale can be correlated with all features in the ime scale as found in the seismic section. However, this sonic log must beThe sonic log enables all atures along the depth scale can be correlated with all features in the time scale as found in the seismic section. However, this sonic log must be corrected to the time-depth curve obtained from the check shot survey. The problem arises when some zones around the borehole such as the invaded zones or the flushed zones exhibit dispersive properties. This dispersive properties causes discrepancies between integrated sonic transit time and the time-depth curve. As a result, the synthetic seismogram generated from the sonic log will not match with the corresponding seismic section. To solve this problem, a practical method for correcting the discrepancies is presented in this paper. Although the method is inspired by paper published in the seventies, but the way to approach the problem is different. This problem looks simple, but its effect on mapping top and bottom of the reservoir is important. An example of implementation from the real field is also given. corrected to the time-depth curve obtained from the check shot survey. The problem arises when some zones around the borehole such as the invaded zones or the flushed zones exhibit dispersive properties. This dispersive properties causes discrepancies between integrated sonic transit time and the time-depth curve. As a result, the synthetic seismogram generated from the sonic log will not match with the corresponding seismic section. To solve this problem, a practical method for correcting the discrepancies is presented in this paper. Although the method is inspired by paper published in the seventies, but the way to approach the problem is different. This problem looks simple, but its effect on mapping top and bottom of the reservoir is important. An example of implementation from the real field is also given.

Petrophysical analysis of well data for clastic reservoir evaluation and determination of the invaded zone effect: A case study of a field in the onshore Niger Delta, South-South, Nigeria

Two reservoirs domiciled in a well have been analysed in this study in order to obtain the quantitative reservoir properties. These quantitative reservoir properties are essential not only for well development and optimization but also for accurate decision-making prior to reservoir exploitation and production. A suite of composite logs comprising the caliper log, gamma-ray (GR) log, density log, resistivity logs and sonic transit time logs obtained from a field in the onshore Niger Delta was used for the petrophysical analysis. From our quantitative results, Reservoir 1 has the potential to produce oil and gas with a total hydrocarbon saturation of 81%. However, due to the invaded zone effect on the reservoir potential, the total hydrocarbon fluid has been partitioned into moveable hydrocarbon of 65% saturation and immoveable hydrocarbon of 16% saturation. This resulted from the mud filtrate invasion of the radially shallow zone which displaced the original fluid while the undisplaced hydrocarbon fluid remained in the pore spaces of the reservoir. The porosity of the reservoir sand is 33% and its effective porosity is 32.5%. The high effective/interconnected porosity and clean nature of the sand which is due to the extremely small volume of shale present in the sand (just 1.0%) clearly shows that the reservoir is highly porous, permeable and producible.The net pay of the reservoir (represented by the thickness TH in Mesh 1) could not be quantitatively determined due to lack of depth information on the logs. The second reservoir (Reservoir 2) has a total hydrocarbon saturation of 94.8% and water saturation of just 5.2%. The total hydrocarbon saturation is partitioned into 82.8% moveable hydrocarbon saturation and 12% immoveable hydrocarbon saturation which resulted from the invaded zone effect. The porosity of the reservoir was determined at 33% and its effective porosity at 32.5%. Again, the high effective/interconnected porosity and clean nature of the sand arising from the extremely small volume of shale present in the sand (0.4%) is a clear indication that the second reservoir is also highly porous, permeable and producible. The net pay of Reservoir 2 (represented by the thickness TH in Mesh 2) is higher compared to Reservoir 1 but could not be evaluated due to absence of depth values on the logs. In the final analysis, the overall results show that the reservoirs are commercially favourable and have the potential to pay back.

New understanding of overpressure responses and pore pressure prediction: Insights from the effect of clay mineral transformations on mudstone compaction

Chemical compaction dominated by clay mineral transformations, commonly develops in deeply buried mudstones, and prominently affects the petrophysical properties of mudstones and fluid flow. This paper takes the Neogene mudstones in the Ledong Slope of the Yinggehai Basin as research object, analyzes the relationship between chemical compaction and overpressure, establishes a new multisegment normal compaction trends (NCTs) and reevaluates the overpressure mechanisms. The results show obvious clay mineral transformation with increasing depth, and the top of the clay mineral transformations is approximately equal to the depth of the overpressure. The logging responses of overpressured mudstones are affected by clay mineral diagenesis, showing a 45% increase in the sonic transit time at 3500-5000 m and a 10% decrease in the density at the same interval, and the density response exhibits a relative lag. A method comprising an inversion algorithm calibrated by measured pressure data is proposed to establish multisegment NCTs. Furthermore, new loading-unloading curves comprising a mechanical loading curve and chemical loading curve are established to reevaluate the overpressure mechanisms. The loading-unloading curves show that the pressure gradient generated by mudstone compaction maybe less than 1.8. While the pressure data with pressure gradient larger than 1.8 fall on the unloading curve, indicating elevated sandstone pressures rather than in situ mudstone pressures. The multisegment NCTs were proven to provide a satisfactory overpressure estimate in the Ledong Slope as well as the relative contribution of different overpressure mechanisms. This paper provides new insight into mudstone compaction and overpressure responses, as well as a meaningful reference for the identification of multiple-overpressure generation mechanisms and the prediction of pore pressure in deeply buried formations.

Effects of Overpressure on Mechanical Properties of Unconventional Shale Reservoirs through Novel Use of a Sonic Overpressure Indicator

Summary Overpressure is a common feature among productive unconventional shale reservoirs, such as the Bone Spring (BSPG) and Wolfcamp (WFMP) Formations of the Delaware Basin (DB) of west Texas and southeastern New Mexico, and is thought to be a strong driver of well productivity. Compared with conventional reservoirs and shales in normal pressured conditions, the effects of overpressure on the mechanical properties of shales is not well understood. Here we present an analysis of overpressure in clay-bearing siliciclastic facies of the BSPG and WFMP Formations of the DB and implications for mechanical properties of the reservoir. Estimation of the effects of overpressure on mechanical properties of unconventional shale reservoirs is determined through use of the sonic overpressure indicator (SOPI). The method requires log model results that accurately characterize variations in lithology and porosity for the formations of interest. The SOPI (ΔT/ΔTN)2, where ΔT is the measured compressional sonic transit time, and ΔTN is the forward-modeled result for normally pressured conditions, can be used with elastic moduli and their interrelationships to compare estimates of mechanical properties including Poisson’s ratio ν, the Biot or effective stress coefficient α, and Young’s modulus E, in normal and overpressured conditions. Results presented here are broadly applicable to overpressured unconventional reservoirs that contain significant clay volume (&amp;gt;0.1 v/v) and exhibit low porosity (&amp;lt;0.08 v/v), comparable to that of siliciclastic-rich facies of the WFMP Formation. To account for increased VP/VS ratio, we regard overpressurization of shaly facies as an irreversible thermodynamic process that transforms a normally pressured siliciclastic system. At stress below the yield point, which is taken as the limit of normal pressure, the system responds elastically to stress; beyond this point, during overpressurization, the system responds as an elastic/plastic medium with strain hardening. We regard elastic moduli as descriptive of mechanical energy stored in this system. This perspective enables Poisson’s ratio for the overpressured system νOP to be computed from an estimate of the normally pressured system νN using (ΔT/ΔTN)2. Overpressure also results in a limited increase of the Biot or effective stress coefficient α. Moreover, recognition that overpressure results in a decrease of Young’s modulus, that is, EOP/EN &amp;lt; 1, provides a means of estimating the amount of strain energy stored by the formation due to overpressurization. We believe that when exposed to lower pressures by wellbore construction, this strain energy stored in overpressured unconventional reservoirs drives creep, which affects interpretations made using geomechanical models. We have developed and tested computational models based on biaxial or plane strain for vertical wells and uniaxial strain for horizontal wells that describe how creep likely affects estimation of minimum horizontal stress Shmin and pore pressure from instantaneous shut-in-pressure (ISIP) measurements. Thus, for overpressured unconventional reservoirs, ISIP determinations differ from tectonic Shmin by an amount related to ν and EOP/EN.

Quantitative Interpretation of Water Sensitivity Based on Well Log Data: A Case of a Conglomerate Reservoir in the Karamay Oil Field

Abstract In the process of oil reservoir development, clay minerals in the pores of a reservoir swell because of water absorption, blocking the pore channels, and decreasing the reservoir permeability. This phenomenon is referred to as reservoir water sensitivity. To develop and protect oil and gas fields more scientifically and effectively and save development cost, we examine the water sensitivity of the low-permeability conglomerate reservoir of the Lower Karamay Formation in the 530-well area of District 8 of the Karamay oil field. Based on multiple linear regression and neural network methods, the quantitative calculation of different types of clay minerals was performed by comprehensively using natural gamma ray, neutron, density, sonic transit time, and X-ray diffraction (XRD) analysis data. The water sensitivity experiment was performed in the study area, and the logging interpretation method of reservoir water sensitivity was established by considering the clay mineral content as the medium; moreover, the reservoir water sensitivity degree was quantitatively explained.

Geological-petrophysical insights in the deep Cambrian dolostone reservoirs in Tarim Basin, China

Cores, thin sections, and cathodoluminescence (CL) analysis were integrated to document the occurrence and petrology of dolomites, and their pore systems in the Cambrian of Tarim Basin, China. Depositional facies, pore types and dolomitization processes of various dolostone reservoir types are determined. Six types of dolomite are recognized, including microbial dolomite, dolomicrite, fabric retentive dolomite, fabric-obliterative dolomite, fine to medium crystalline dolomite cement, and saddle dolomite cement. Pore systems are dominantly vugs, anhydrite dissolution pores, intercrystalline pores, intercrystalline dissolution pores, fabric dissolution pores and microfractures. Four porous dolostone reservoirs include sabkha dolostone, seepage-reflux dolostone, burial dolostone, hydrothermal dolostone. Fractures play an important role in enhancing reservoir quality in dolostone reservoirs. Conventional wireline logs and image logs are calibrated with cores and related thin sections. Sabkha dolostone reservoirs are characterized by dark and bright spots on the image logs. Seepage-reflux dolostone reservoirs are related to high-energy depositional facies and are characterized by low gamma-ray amplitude, increasing sonic transit time and neutron porosity but reducing bulk density values. Evident dark spots (vugs) are recognized on image logs, and all three porosity logs suggest relatively high reservoir quality in burial dolostone reservoirs. Hydrothermal dolostone reservoirs are recognized by high gamma-ray response caused by hydrothermal minerals (fluorite), and porosity curves indicate good reservoir quality, which is supported by dark spots (vugs) on the image logs. Rapid decrease in resistivity, increasing in sonic transit time values, and the dark sinusoidal waves on the image logs are typical of fractured dolostone reservoirs. The distribution of dolostone reservoirs in each well is predicted using a comprehensive analysis of conventional and image logs, and they are calibrated with oil test data. The research provides insights in the analysis of genetic model of deeply buried dolostone reservoirs and establishes the predictable model for reservoir quality in dolostones via well logs.

Application of Various Machine Learning Techniques in Predicting Total Organic Carbon from Well Logs.

Unconventional resources have recently gained a lot of attention, and as a consequence, there has been an increase in research interest in predicting total organic carbon (TOC) as a crucial quality indicator. TOC is commonly measured experimentally; however, due to sampling restrictions, obtaining continuous data on TOC is difficult. Therefore, different empirical correlations for TOC have been presented. However, there are concerns about the generalization and accuracy of these correlations. In this paper, different machine learning (ML) techniques were utilized to develop models that predict TOC from well logs, including formation resistivity (FR), spontaneous potential (SP), sonic transit time (Δt), bulk density (RHOB), neutron porosity (CNP), gamma ray (GR), and spectrum logs of thorium (Th), uranium (Ur), and potassium (K). Over 1250 data points from the Devonian Duvernay shale were utilized to create and validate the model. These datasets were obtained from three wells; the first was used to train the models, while the data sets from the other two wells were utilized to test and validate them. Support vector machine (SVM), random forest (RF), and decision tree (DT) were the ML approaches tested, and their predictions were contrasted with three empirical correlations. Various AI methods' parameters were tested to assure the best possible accuracy in terms of correlation coefficient (R) and average absolute percentage error (AAPE) between the actual and predicted TOC. The three ML methods yielded good matches; however, the RF-based model has the best performance. The RF model was able to predict the TOC for the different datasets with R values range between 0.93 and 0.99 and AAPE values less than 14%. In terms of average error, the ML-based models outperformed the other three empirical correlations. This study shows the capability and robustness of ML models to predict the total organic carbon from readily available logging data without the need for core analysis or additional well interventions.

In-situ stresses, abnormal pore pressures and their impacts on the Triassic Xujiahe reservoirs in tectonically active western Sichuan basin

Pore pressures in the Xujiahe gas-bearing formations are highly overpressured in the western Sichuan basin. The abnormally high overpressure and strong tectonic stress cause very high horizontal stresses. The high in-situ stresses, tectonic stress regimes, and tight formations severely impact development and production of the Xujiahe tight sandstone reservoirs. Overpressure generation mechanisms are analyzed based on measured data in several dozen wells in the studied gas fields. It is found that the sonic or seismic transit time can be used to predict pore pressure. Two major reservoirs (Xu2 and Xu4) belong to different fluid compartments with distinct pore pressure gradients. The Xu4 has a much higher pore pressure gradient (20 MPa/km) than the Xu2 (15.6 MPa/km).Measured data from hydraulic fracturing tests and borehole image logs in vertical wells are analyzed. The results show that the minimum horizontal stress gradient in the Xu4 reservoir reaches as high as 24 MPa/km, close to the overburden gradient. The Xu2 reservoir has a slightly lower minimum horizontal stress gradient. The high minimum horizontal stress and high rock strength cause abnormally high formation breakdown pressure gradient (24.8–34 MPa/km). The high breakdown pressure, high propagation pressure, and unfavorable stress regime result in a great difficulty for the reservoir stimulation. One of the major impacts is that hydraulic fracturing operations can only create very short hydraulic fractures, which markedly limits reservoir productivity. Based on field measurements, in-situ stress determination and assessment are given, and a new method for horizontal stress estimation is proposed. Accordingly, reliable prediction of formation breakdown and propagation pressures are proposed, which are more suitable for the tight reservoirs. Recommendations are given for effective development of the tight reservoirs with impacts of high in-situ stresses.

Qualitative and quantitative prediction of diagenetic facies via well logs

Drilled cores, thin section petrography, X-ray diffraction (XRD), scanning electron microscopy (SEM), routine core analysis (RCA), and mercury injection capillary pressure (MICP) tests were used to establish a predictable model for diagenetic facies and reservoir quality using well logs in sandstones of the Paleogene Dongying Formation in Nanpu Sag, Bohai Bay Basin, Eastern China. The results show that the sandstones are texturally and compositionally immature, and have primary intergranular pores, secondary intergranular and intragranular pores, moldic pores, and micro-pores associated with diagenetic clay minerals. The sandstones have undergone low to moderate mechanical compaction, but a high degree of framework-grain dissolution. Diagenetic minerals are dominantly of carbonates (calcite, dolomite, and Fe-dolomite) and clay minerals (illite, mixed layer illite/smectite and kaolinite). Four types of diagentic facies are recognized: tightly compacted, carbonate cemented, clay mineral filling, and dissolution facies. Porosity, permeability and oil saturation ranges among these diagenetic facies were determined from routine core analyses. The diagenetic facies were then translated to conventional well logs by core-log calibration, and the well log expressions (in terms of natural gamma ray, neutron porosity, bulk density, sonic transit time, and resistivity logs) of various diagenetic facies were summarized. The mineralogical composition obtained from the Elemental Capture Spectroscopy (ECS) logs was then used for the diagenetic facies prediction. The diagenetic facies predicted with the comprehensive conventional log analyses were combined with the ECS log profile, and were calibrated with the XRD, SEM analysis and logging interpretation results. For wells without ECS log, the diagenetic facies can be quantitatively predicted by the mathematical calculations of reservoir quality index (RQI) via the GR, AC and DEN log curves. Reservoir quality or oil bearing properties can be evaluated and predicted by translating the diagenetic facies to well logs.

Fluid Property Discrimination in Dolostone Reservoirs Using Well Logs

AbstractThe Ordovician Majiagou Formation is one of the main gas‐producing strata in the Ordos Basin, China. The identification of hydrocarbon‐bearing intervals via conventional well logs is a challenging task. This study describes the litholog of Ma 5 (Member 5 of Majiagou Formation) dolostones, and then analyzes the responses of various conventional well logs to the presences of natural gas. The lithology of the gas bearing layers is dominantly of the dolomicrite to fine to medium crystalline dolomite. Natural gas can be produced from the low resistivity layers, and the dry layers are characterized by high resistivities. Neutron‐density crossovers are not sensitive to the presences of natural gas. In addition, there are no significant increases in sonic transit times in natural gas bearing layers. NMR (nuclear magnetic resonance) logs, DSI (Dipole Sonic Imager) logs and borehole image logs (XRMI) are introduced to discriminate the fluid property in Majiagou dolostone reservoirs. The gas bearing intervals have broad NMR T2 (transverse relaxation time) spectrum with tail distributions as well as large T2gm (T2 logarithmic mean values) values, and the T2 spectrum commonly display polymodal behaviors. In contrast, the dry layers and water layers have low T2gm values and very narrow T2 spectrum without tails. The gas bearing layers are characterized by low Vp/Vs ratios, low Poisson's ratios and low P‐wave impedances, therefore the fluid property can be discriminated using DSI logs, and the interpretation results show good matches with the gas test data. The apparent formation water resistivity (AFWR) spectrum can be derived from XRMI image logs by using the Archie's formula in the flushed zone. The gas bearing layers have broad apparent formation water resistivity spectrum and tail distributions compared with the dry and water layers, and also the interpretation results from the image logs exhibit good agreement with the gas test data. The fluid property in Majiagou dolostone reservoirs can be discriminated through NMR logs, DSI logs and borehole image logs. This study helps establish a predictable model for fluid property in dolostones, and have implications in dolostone reservoirs with similar geological backgrounds worldwide.

Data-driven model for shear wave transit time prediction for formation evaluation

Sonic well logs provide a cost-effective and efficient non-destructive tool for continuous dynamic evaluation of reservoir formations. In the exploration and production of oil and gas reservoirs, these sonic logs contain crucial information about the formation. However, shear sonic logs are not acquired in all oil and gas exploration wells. More so, many offset wells are not run with the most recent sonic logging tools capable of measuring both shear and compressional sonic transit times due to the relatively high costs of running such equipment. And in wells where they are deployed, they are run only over limited intervals of the formation. Such wells lack continuous shear wave transit time measurements along the formation. In this study, an exponential Gaussian process model is presented. The model accurately predicts the shear wave transit times in the formations which lack reliable shear wave transit time measurements. The proposed model is developed using an array of well logs, namely depth, density, porosity, gamma ray, and compressional transit time. A Monte Carlo simulation is used to quantify the proposed model uncertainty. The shear sonic transit time predictions are used to estimate some formation deformation properties, namely Young’s modulus and Poisson’s ratio of a reservoir formation. The results suggest that shear transit time can be represented and predicted by Gaussian-based process model with RMSE, R2, and MSE of 11.147, 0.99, and 124.6, respectively. The proposed model provides a reliable and cost-effective tool for oil and gas dynamic formation evaluation. The findings from this study can help for better understanding of shear transit times in formations which do not have multipole sonic logs or where data have been corrupted while logging in the Niger Delta.