Integral Inversion Research Articles

A non-contact quantitative risk assessment framework for translational highway landslides: Integration of InSAR, geophysical inversion, and numerical simulation

A non-contact quantitative risk assessment framework for translational highway landslides: Integration of InSAR, geophysical inversion, and numerical simulation

Unlocking thin sand potential: a data-driven approach to reservoir characterization and pore pressure mapping

The gradual decline of production of the major fields of the Indus Basin has influenced the field pressure. Therefore, the prospects need to be assessed by the petro-elastic relationship. This study highlights the value-addition for identifying gas pockets within the E-Sand of the Lower Goru Formation through estimating and populating petro-elastic properties. A probabilistic neural network (PNN) was employed to develop the relationship of pore pressure to the inverted properties. The outcomes will help identify the high-pressure areas in accordance with the petro-elastic relationship for optimizing production strategy. In order to evaluate the petro-elastic relationship, stochastic seismic inversion was employed to holistically capture the reservoir’s variability. Pore pressure volumetric was estimated from inverted attributes using a PNN, a non-linear algorithm that was more suited to heterogeneous reservoirs. The main objective of the research is achieved via enhanced resolution of elastic properties attained through the integration of stochastic inversion and PNN processes. The high-resolution elastic properties including P-impedance, S-impedance, and Vp/Vs ratio can delineate the heterogeneities more profoundly. The petrophysical volumes obtained via enhanced inverted properties combined with pore-pressure through PNN illuminated the potential facies and the correlations in predicting the properties. The procedure provides a linkage of elastic, petrophysical, and geomechanical properties that is helpful for professionals covering a broad field of the petroleum sector. The developed workflow can be followed globally where problems occur regarding heterogeneities and early depletion.

Integration of geographic features and bathymetric inversion in the Yangtze River's Nantong Channel using gradient boosting machine algorithm with ZY-1E satellite and multibeam data

This study investigates the integration of geographic features from ZY-1E satellite data with advanced machine learning techniques to enhance water depth inversion in the Yangtze River's Nantong Channel. Utilizing the Gradient Boosting Machine (GBM) and its geospatially enhanced version, GBM-Lon./Lat., significant improvements in modeling precision were observed, as reflected by lower RMSE and higher R² values compared to traditional depth inversion methods. The research underscores the benefits of incorporating geospatial data, which allows for a more nuanced understanding of the hydrological dynamics and facilitates more accurate predictions in the turbid waters of the channel. Challenges such as atmospheric effects, water turbidity, and data acquisition issues under variable weather conditions were identified. The study proposes further optimization of these models to handle diverse environmental conditions and enhance the accuracy of bathymetric mapping. The integration of machine learning with remote sensing not only supports navigational safety and efficient waterway management but also contributes significantly to environmental monitoring and sustainable riverine infrastructure development.

Deep-learning optimization using the gradient of a custom objective function: A full-waveform inversion example study on the convolutional objective function

The integration of conventional high-performance full-waveform inversion (FWI) algorithms with deep-learning frameworks is an innovative and promising research direction with the potential to enhance and broaden the application prospects of this field significantly. Automatic differentiation with backpropagation techniques can derive the gradients of model parameters in an equivalent manner to that of adjoint methods; however, it requires a substantial amount of computer memory, particularly when considering the time-step layers. In addition, some excellent objective functions suitable for FWI are not available in deep-learning frameworks. In comparison, the adjoint method, based on effective boundary storage technology, offers greater practicality for calculating the gradients of various objective functions. Therefore, this paper develops a novel approach toward FWI that integrates deep-learning optimization with high-performance gradient computation. In particular, our method inputs model parameter gradients from a custom objective function into the deep-learning framework. Herein, we use the acoustic equation with variable density as an example to demonstrate how a convolutional objective function, along with its corresponding velocity and density gradients, can be used for optimized inversion, multiscale inversion, and deep network parameterization-based multiscale inversion within the deep-learning framework. This approach provides a paradigm for deep-learning-optimized FWI, which we apply to synthetic and field data scenarios.

Evaluation of Carbonate Reservoir with Integration of Petrophysical Analysis and Seismic Impedance Inversion in Banggai Basin, Sulawesi

Banggai Basin is controlled by the collision between Banggai-Sula micro-continent and East Sulawesi ophiolite belt, forming a thrust-sheet anticline structure that has potential to trap hydrocarbon. The Siotu-1 well was drilled right into the thrust-sheet anticline structure targeting Miocene carbonate platform layer. The same structure has proven to be an oil trap at the Tiaka oilfield which is located adjacent to the Siotu-1 well. This study aims to evaluate the presence of hydrocarbon in the Siotu-1 well with characterization of the Miocene carbonate reservoir. It is carried out by integrating petrophysical analysis from the Siotu-1 well log data and 2D seismic impedance inversion of the Tarib-1 line. Five reservoir zones have shale content values range of 21.91-37.53%, effective porosity values range of 9.7-14.62%, water saturation values range of 88.5-98.27%, and permeability values range of 2.53-8.16 mD. The results of the porosity calculation are validated by the model of seismic inversion where the reservoir zone with an acoustic impedance in the range of 7600-13450 ((m/s)/(g/cc)) is a carbonate layer with fair to excellent porosity (10 to> 25%) and reservoir zone with acoustic impedance values range of 13450-16200 ((m/s)/(g/cc)) as carbonate layer with poor porosity (< 10%). The net pay zone in reservoir zone 4 with a thickness of 4.5 feet and a net-to-gross of 0.006 are shown as the lumping result. It is concluded that Miocene carbonate reservoir in the Siotu-1 well met the criteria as a good reservoir rock but insignificant of hydrocarbon potential for further development.

Fabrication of a novel composite nanofiltration membrane with effective treating dye wastewater via integration of phase inversion and interfacial polymerization

In this work, we proposed using the combination of nonsolvent induced phase separation (NIPS) technology and interfacial polymerization (IP) process upon polyvinylidene fluoride (PVDF) substrate as a novel and simple method for preparing high-flux composite nanofiltration membranes. The surface chemical properties, physical structure, hydrophilicity, zeta potential, and permeation experiments of the composite membranes that were prepared with graphene oxide (GO), cyclodextrin (CD), or GO@CD as aqueous monomers were studied. The results indicated that the selective polyester active layer successfully covered the PVDF substrate, and the optimal GO@CD50/PVDF membrane was fabricated using the modification conditions of 1.0 wt% of GO@CD50, 5 min of phase transition time, 0.2 wt% of TMC, and 60 min of IP reaction time, which achieved a high pure water flux (84.8 L m−2 h−1 bar−1) and dye permeation flux (48.7 L m−2 h−1 bar−1 for Rose Bengal); in addition, the dye rejection rate was also high (e.g., 99.6 % for Rose Bengal). Besides, CD nanomaterials can be effectively inserted into the GO lamella to increase the layer spacing of GO sheets and expose more oxygen-containing functional groups; this, in turn, enhances the long-term stability and permeability of the membrane. This study provides practical ways to treat dyes wastewater.

Supervised machine learning application of lithofacies classification for a hydrodynamically complex gas - condensate reservoir in Nam Con Son basin

Conventional integration of rock physics and seismic inversion can quantitatively evaluate and contrast reservoir properties. However, the available output attributes are occasionally not a perfect indicator for specific information such as lithology or fluid saturation due to technology constraints. Each attribute commonly exhibits a combination of geological characteristics that could lead to subjective interpretations and provides only qualitative results. Meanwhile, machine learning (ML) is emerging as an independent interpreter to synthesise all parameters simultaneously, mitigate the uncertainty of biased cut-off, and objectively classify lithofacies on the accuracy scale.&#x0D; In this paper, multiple classification algorithms including support vector machine (SVM), random forest (RF), decision tree (DT), K-nearest neighbours (KNN), logistic regression, Gaussian, Bernoulli, multinomial Naïve Bayes, and linear discriminant analysis were executed on the seismic attributes for lithofacies prediction. Initially, all data points of five seismic attributes of acoustic impedance, Lambda-Rho, Mu-Rho, density (ρ), and compressional wave to shear wave velocity (VpVs) within 25-metre radius and 25-metre interval offset top and base of reservoir were orbitally extracted on 4 wells to create the datasets. Cross-validation and grid search were also implemented on the best four algorithms to optimise the hyper-parameters for each algorithm and avoid overfitting during training. Finally, confusion matrix and accuracy scores were exploited to determine the ultimate model for discrete lithofacies prediction. The machine learning models were applied to predict lithofacies for a complex reservoir in an area of 163 km2.&#x0D; From the perspective of classification, the random forest method achieved the highest accuracy score of 0.907 compared to support vector machine (0.896), K-nearest neighbours (0.895), and decision tree (0.892). At well locations, the correlation factor was excellent with 0.88 for random forest results versus sand thickness. In terms of sand and shale distribution, the machine learning outputs demonstrated geologically reasonable results, even in undrilled regions and reservoir boundary areas.

New evidence of Late Cretaceous magmatism on the offshore central West Iberian Margin (Estremadura Spur) from potential field data

The West Iberian Margin (WIM) is a key example of a magma-poor passive margin, punctuated by several post-rift magmatic manifestations that are part of the Late Cretaceous Atlantic Alkaline Province. In this work, potential field (gravity and magnetics) data, constrained by 3D multichannel seismic reflection data, are used to describe and characterise the geometry and nature of magmatic features located offshore the central segment of the margin, the Estremadura Spur. The estimated geometry and nature of the magmatic features was achieved through the integration of 3D gravity and magnetic inversion and 2D magnetic forward modelling. The results provide an insightful 3D subsurface model revealing that: 1) the Estremadura Spur Intrusion represents a 28 × 15 km wide laccolith with an overall granitic nature and an estimated density of 2490–2640 kg/m3 and 0.01–0.05 SI magnetic susceptibility, 2) the 26 × 17 km Fontanelas buried volcano is dominantly basaltic, with density values of 2500–2821 kg/m3 and magnetic susceptibility of 0.01 to 0.0875 SI, and 3) multiple sill complexes intruded the region, thus producing a higher magnetic background on otherwise inconspicuous anomalies The models allowed achieving a confident fit suggesting that both the ESI and the Fontanelas volcano are coeval with the outcropping magmatic features from this same magmatic event. Additionally, the results support that Late Cretaceous alkaline magmatism on the West Iberian Margin is more significant than anticipated and provide further evidence to clarify unclear geometrical aspects of similar intrusions observed onshore. Acknowledging the geometry and nature of these magmatic entities allows to better understand the role of post-rift intra-plate magmatism on continental hyper-extended rifted margins by clarifying how shallow plumbing systems evolve in these settings.

Facies modelling of Ngimbang Clastics interval of Ngimbang formation with the integration of rock physics analysis and simultaneous inversion, North East Java Basin

Facies modelling of Ngimbang Clastics interval of Ngimbang formation with the integration of rock physics analysis and simultaneous inversion, North East Java Basin

Integration of sequence stratigraphy, rock physics and AI inversion to determine the prospective reservoir in the shelf environment of the Tarakan Basin, Indonesia

The Tarakan Basin is one hydrocarbon basin in Indonesia with approximately 40 discoveries and more than 1000 MBOE reserves. This study discusses an approach to integrate the sequence stratigraphy, rock physics and acoustic impedance (AI) inversion analysis to determine the prospective reservoirs in the basin. PRG-1 well data is used in the sequence stratigraphy and rock physic analysis. The sequence stratigraphy analysis of PRG-1 shows that there are three system tracts: transgressive, low stand tract and high stand system tracts. The integration of sequence stratigraphy, rock physics and log data analysis show that the prospective reservoir interval in PRG-1 well is located at a depth of 4730-4780 feet. It is characterized by low gamma ray, low NPHI, low density and high resistivity. The prospective interval was deposited in early Pliocene as Tarakan Formation in the low stand system tract of shelf depositional environment. The AI map shows that the distribution of the prospective is around the PRG-1 and in the eastern part of the area.

Integration of Post-stack Inversion and Rock Physics to Determine Sandstone Reservoir Quality: Barakan Sub-basin case

Barakan Sub-basin is assessed as potential basin for hydrocarbon reserves in the eastern region of Indonesia because it is adjacent to Masela block giant gas field. Reservoir rocks in this sub-basin are sandstones from Middle Jurassic (Lower Flamengo Formation) until Oligocene (Adi member Formation). Main sandstone reservoir rocks are knowingly studied to have good porosity in Upper Flamengo, Kopae, Ekmai and Adi member Formations. But, there is no significant study to determine sandstone reservoir distribution that have good porosity quality. Therefore, an integrated method of inversion and rock physics study are needed to determine sandstone reservoir quality. This study uses 2D marine seismic post-stack time migration and 2 wells namely Barakan-1 and Koba-1 wells. Sensitivity analysis with cross-plot of gamma ray log versus acoustic impedance values range of 20-60 API and 9000-42000 (ft/s)*(g/cc) shows a strong correlation of good porosity sandstone to low impedance in Ekmai Formation of both wells. Model based of post-stack inversion reveals sandstone distribution in Ekmai Formation of both wells. Time structure maps of top and bottom horizons in Ekmai Formation indicates Barakan-1 well within anticline height structure and Koba-1 well are deposited in a middle of sub-littoral environment.

Fluid and lithofacies prediction based on integration of well-log data and seismic inversion: A machine-learning approach

Seismic prediction of fluid and lithofacies distribution is of great interest to reservoir characterization, geologic model building, and flow unit delineation. Inferring fluids and lithofacies from seismic data under the framework of machine learning is commonly subject to issues of limited features, imbalanced data sets, and spatial constraints. As a consequence, an extreme gradient boosting-based workflow, which takes feature engineering, data balancing, and spatial constraints into account, is proposed to predict the fluid and lithofacies distribution by integrating well-log and seismic data. The constructed feature set based on simple mathematical operations and domain knowledge outperforms the benchmark group consisting of conventional elastic attributes of P-impedance and [Formula: see text] ratio. A radial basis function characterizing the weights of training samples according to the distances from the available wells to the target region is developed to impose spatial constraints on the model training process, significantly improving the prediction accuracy and reliability of gas sandstone. The strategy combining the synthetic minority oversampling technique and spatial constraints further increases the F1 score of gas sandstone and also benefits the overall prediction performance of all of the facies. The application of the combined strategy on prestack seismic inversion results generates a more geologically reasonable spatial distribution of fluids, thus verifying the robustness and effectiveness of our workflow.

Integration of new interpretation, attribute analysis and inversion results for the Dampier Sub-basin, North West Shelf of Australia

The Davros-Typhon Multi-Client 3D surveys are located approximately 70km from the north-west coast of Australia, largely covering the NE trending Dampier Sub-basin and straddling the Rankin Trend within the Northern Carnarvon Basin. The basins within the North West Shelf formed as a result of seafloor spreading, associated with the breakup of the North West margin of East Gondwana. The combined, contiguous Davros-Typhon survey areas cover a number of significant discoveries and producing fields, which include both oil and gas accumulations. The key objective of the survey was to enhance the imaging of Triassic to Lower Cretaceous reservoir units and to develop a new interpretation framework, made possible by the modern broadband acquisition parameters and advanced processing techniques. Challenges associated with imaging and interpretation include the effects of high velocity carbonate overburden, steeply dipping structures, fault shadow and structural complexity at depth, which is critical for evaluation of reservoir targets. A major reprocessing effort was undertaken to further mitigate these issues, which included Davros and a number of adjacent existing 3D surveys, resulting in the Typhon Multi-Client 3D. CGG Multi-client and New Ventures geoscientists, in collaboration with CGG Seismic Imaging, have undertaken new interpretation and amplitude versus offset (AVO) inversion analysis using subsets of the Typhon 3D. The resulting volume-based attribute analysis and integration of new AVO inversion results demonstrates enhanced attribute quality for the reprocessed data and provides a platform for quantitative analysis over a large area of the Northern Carnarvon Basin.

Integration of knowledge-based seismic inversion and sedimentological investigations for heterogeneous reservoir

Abstract Conventional geological modelling methods are not capable to provide precise and comprehensive model of the subsurface structures, when dealing with insufficient data. Knowledge based methods employing rule bases techniques are found vast applications in geoscience studies. These methods are applicable for petroleum reservoir geological modelling and characterizations, specifically for geologically complex structures. In this study, we present a knowledge based seismic acoustic impedance inversion method which employs rule based method for porosity estimation. The back propagation algorithm and the fuzzy neural network are also used in the methodology for parameter optimization and definition of nonlinear relationship between seismic attributes and porosity of the reservoir rock. The methodology initiates by seismic acoustic impedance inversion, followed by conventional porosity estimation. Subsequently, a knowledgebase was designed by investigation on more than 24 published case studies. This knowledgebase was used for definition of rules and optimization number of rules and improve efficiency of the inference engine. The porosity model obtained by conventional method in previous step would be used for primary evaluation of the rules. The extracted rules and optimized number rules then would be used for rule-based porosity estimation. The methodology was applied on a petroleum field containing two heterogeneous reservoir formations. Result of application of the proposed approach was evaluated with core analysis, thin sections and drilling data. Consistency of result obtained by the proposed method with geological data has shown its capability to resolve problem of insufficient data in reservoir geological modelling.

Integration of gravity and magnetic inversion for geothermal system evaluation in Suli and Tulehu, Ambon, eastern Indonesia

The geothermal field of Suli and Tulehu is located in the eastern part of the Ambon Island, administratively situated in central Maluku regency, Maluku province, Indonesia. In the year 2017, the measurement of the Earth’s gravity and magnetic field has been done to evaluate the geothermal system in Suli and Tulehu. The application of gravity and magnetic methods was applied to determine the subsurface geological model describing geothermal sources, reservoirs, and faulting as a fluid conduit in the study area. Bouguer anomalies are positive in the range from 93 to 105 mGal, while magnetic anomalies are negative to positive in the range from − 656 to 310 nT, generally southwest to northeast. Geothermal manifestations in the form of hot springs associated with low residual Bouguer and magnetic anomalies positioned on the Banda-Hatuasa, Banda, and Huwe fault lines related to the alteration zone. Euler deconvolution analysis of the complete Bouguer anomaly data shows the maximum depth of fault detected at 1200 m. Gravity and magnetic inversion show high density and susceptibility contrast of rocks associated with Eriwakang and Simalopu pyroclastic rocks as geothermal sources. Low density and susceptibility contrast is estimated to be geothermal reservoirs due to interactions with geothermal source rocks below. A geological conceptual model based on the inversion modeling of gravity and magnetic data provides clear and comprehensive information on the geothermal systems in Suli and Tulehu, Ambon, the eastern part of Indonesia.

The igneous rock intrusion beneath Ambon and Seram islands, eastern Indonesia, based on the integration of gravity and magnetic inversion: its implications for geothermal energy resources

Ambon and Seram islands in eastern Indonesia are located in the convergence zone of three major plates, causing this area to have many geothermal manifestations on the surface. A subsurface geological study on Ambon and Seram has been conducted based on the integration of gravity and magnetic inversion modeling to determine the intrusion of igneous rock as a potential source of geothermal energy. The gravity and magnetic inversion utilized gravity and magnetic anomaly data from the World Gravity Map (WGM) and the Earth Magnetic Anomaly Grid (EMAG), as well as the CRUST 1.0 global earth crust model, as the constraints. In general, gravity and magnetic anomalies in the study area are compatible, in which the southern part of Ambon is dominated by high anomalies and the northern part is covered by low anomalies associated with the Seram trench. The high anomaly is thought to be related to the intrusion of igneous rocks underneath the surface that have cooling and are rich in magnetic minerals, while the low anomaly is related to the destruction zone due to subduction in the north of Seram. The gravity inversion shows the distribution of subsurface rock density ranging from 1.86 g cm-3 to 2.82 g cm-3, while magnetic inversion produces rock susceptibility contrast ranging from -0.0034 SI to 0.0058 SI. Based on Curie point depth analysis of magnetic data, the bottom depth of the magnetic source is estimated at 25 km. The 3D subsurface model of Ambon and Seram based on gravity and magnetic inversion shows a massive and evident intrusion pattern with high rock density and susceptibility contrast down to a depth of 10 km. The distribution of high density and susceptibility is thought to have implications for geothermal resources in Ambon and Seram.

Integration of rock physics and seismic inversion for rock typing and flow unit analysis: A case study

ABSTRACTRock typing and flow unit detection are more challenging in clastic reservoirs with a uniform pore system. An integrated workflow based on well logs, inverted seismic data and rock physics models is proposed and developed to address such challenges. The proposed workflow supplies a plausible reservoir model for further investigation and adds extra information. Then, this workflow has been implemented in order to define different rock types and flow units in an oilfield in the Persian Gulf, where some of these difficulties have been observed. Here, rock physics models have the leading role in our proposed workflow by providing a diagnostic framework in which we successfully differentiate three rock types with variant characteristics on the given wells. Furthermore, permeability and porosity are calculated using the available rock physics models to define several flow units. Then, we extend our investigation to the entire reservoir by means of simultaneous inversion and rock physics models. The outcomes of the study suggest that in sediments with homogeneous pore size distribution, other reservoir properties such as shale content and cementation (which have distinct effects on the elastic domain) can be used to identify rock types and flow units. These reservoir properties have more physical insights for modelling purposes and can be distinguished on seismic cube using proper rock physics models. The results illustrate that the studied reservoir mainly consists of rock type B, which is unconsolidated sands and has the characteristics of a reservoir for subsequent fluid flow unit analysis. In this regard, rock type B has been divided into six fluid units in which the first detected flow unit is considered as the cleanest unit and has the highest reservoir process speed about 4800 to 5000 mD. Here, reservoir quality decreases from flow unit 1 to flow unit 6.

A Facile and Scalable Fabrication Procedure for Thin-Film Composite Membranes: Integration of Phase Inversion and Interfacial Polymerization.

Conventional dense thin-film composite (TFC) membranes evince a universally low water permeability, the increase of which typically relies on introducing additional transport channels based on intricate steps within a membrane preparation process. In this study, we reported a novel and simplified procedure for the fabrication of high-performance TFC membranes. Specifically, the dissolution of aqueous monomers in the casting solution was utilized for the following interfacial polymerization (IP). Since the monomers diffused to the water bath during phase inversion, the control of precipitation time enabled an effective regulation of the monomer concentration in the formed polymeric substrates, where the IP reaction was initiated by the addition of the organic phase. The entire and uniform embedment of aqueous monomers inside the substrates contributed to the formation of ultrathin and smooth selective layers. An excellent separation performance (i.e., water permeability: 34.7 L m-2 h-1 bar-1; Na2SO4 rejection: ∼96%) could be attained using two types of aqueous monomers (i.e., piperazine and β-cyclodextrin), demonstrating the effectiveness and universality of this method. Compared to the conventional immersion-based process, this novel procedure shows distinct advantages in reducing monomer usage, shortening the production cycle, and achieving a more superior membrane performance, which is highly promising for large-scale membrane manufacture.

Near-surface geophysical surveys for bedrock investigation and modeling for grain silos site, Yanbu City, Western Saudi Arabia

The present work highlights results of near-surface geophysical surveys for construction grain silos site at marine Yanbu port in Yanbu province, western Saudia Arabia. Three techniques represented by seismic refraction tomography, electrical resistivity tomography, and time domain electromagnetic, were conducted to investigate the bedrock and overlying sediments. The geophysical surveys highlight the vertical and lateral distribution of the ground velocity and resistivity parameters. The comparison and integration of geophysical inversion and interpretation with borehole data were successfully used in mapping the depth of the bedrock and defining stratigraphic type and thickness of the overlying sediments. According to the investigated depth reached by geophysical investigation, the prospected sediments reveal three sediment layers that composed the prospected soil. The study terrain seems to be suitable for the construction of grain silos. On the other hand, seawater introduced at shallow depths should be a troublesome element in geotechnical construction; hence the requirement for additional safety measures to improve the watertightness of the soil.

Two-dimensional complex shear modulus imaging of soft tissues by integration of Algebraic Helmoltz Inversion and LMS filter into dealing with noisy data: a simulation study.

Elasticity and viscosity of soft tissues can be obtained from the complex shear modulus imaging (CSMI). CSMI is often used not only to investigate the structure of tissues but also to detect tumors in tissues. One of the most popular ways to categorize the methods used in CSMI is into quasi-static and dynamic methods. In the dynamic method, a force excitation is used to create the shear wave propagation, and the particle velocities are measured to extract their amplitude and phase at spatial locations. These parameters are then employed to directly or indirectly estimate the Complex Shear Modulus (CSM) represented by elasticity and viscosity. Algebraic Helmholtz Inversion (AHI) algorithm provides the direct estimation of CSM using the Finite Difference Time Domain (FDTD) technique. The limitation of this method, however, is that the noise generated from measuring the particle velocity strongly degrades the accuracy of the estimation. To overcome this problem, we proposed in this paper an adaptive AHI (AAHI) algorithm that offers a good performance in CSMI with a mean error of 2.06%.