Density Logs Research Articles

Deriving mineral moduli of the noncarbonate fraction in a marly chalk reservoir using petrophysical logging data and an isoframe model

Knowledge of the elastic properties of clays and clay-rich sediments is essential when performing quantitative seismic interpretation and geomechanical modeling of reservoirs where they are present. Such is the case for the marly chalk reservoirs of Lower Cretaceous age in the Danish North Sea. We develop a new method to derive the elastic properties of the clay-rich insoluble residue (IR) fraction in marly chalks, using petrophysical logs and rock physical modeling. We calculate compressional ( M), shear ( G), and bulk ( K) moduli of the formation along the borehole using the density and sonic (shear and compressional) logs and model the elastic properties of minerals using the isoframe (IF) rock-physics model. In our model, the marly chalk is composed of two materials: calcite, with well-known elastic properties, and the IR fraction, whose elastic properties are the sought variable. We systematically vary the assumed elastic properties of the IR fraction, each time achieving equality between the measured and modeled elastic moduli solving for the IF values of calcite ([Formula: see text]) and IR fraction ([Formula: see text]) for each of the three elastic parameters one at a time, deriving the modeled values for the other two. We add up the error between the modeled and logged values for each set of assumed elastic properties of the IR fraction. We adopt the values of [Formula: see text] and [Formula: see text] that minimize the compound error as the actual elastic properties of the IR fraction. We run the algorithm in the Boje-2C and Valdemar-2H wells in different stratigraphic units of the Lower Cretaceous: the Fanø Formation and the marly chalk of the Tuxen and Sola formations. Elastic properties derived for the IR fraction compare well to data for wet clays from ultrasonic velocity laboratory tests. In a clean chalk section (almost 100% calcite), the model outputs values similar to calcite.

Gravity imaging of sub-Zechstein geological structures in the UK sector of the North Sea using the gravity layer stripping method

Extensive geophysical databases, covering the UK sector of the North Sea, have been used to gravity layer strip the sedimentary layers down to the base Zechstein so that the gravity response of the Carboniferous and deeper strata can be identified and structurally interpreted. To achieve this, the average bulk density grids for each layer were derived using Gardner's functions derived from well velocity and density logs. The resulting residual gravity response of each layer and the Moho response were then removed from the Free air gravity anomaly to generate the isostatic gravity response of the crust below the base Zechstein. This gravity response was used to re-evaluate the British Geological Survey interpretation over the Mid North Sea High (MNSH) and was able to identify the same crustal structures. Using the tilt derivative method, a positive gravity anomaly was found to parallel the central fracture zone that forms a northern extension of the Dowsing fault zone. This anomaly can be traced north across the MNSH with offsets coinciding with the WSW–ENE basement lineaments. To the south, the southern North Sea basin is well defined by the stratigraphic layer depth and thickness maps as well as the residual gravity maps which identify the structures associated with the low-density Carboniferous Coal Measures.

Secondary porosity classification in heterogeneous carbonate reservoirs based on the sonic log data

Many carbonate oil and gas reservoirs are characterized by the presence of secondary pores: vugs and fractures. To estimate the volume of oil and gas in carbonate rocks with double porosity, the information about secondary pore structure is required. In the present work we propose a simple and easy to code methodology to determine secondary pore types in carbonate sediments using acoustic well log data. The methodology is based on the comparison of the normalized differences between the calculated and measured in a borehole velocity of the compressional and shear waves. The results were obtained for the rocks with polymineral skeleton (limestone and dolomite). The mineral concentration is estimated by the solution of the inverse problem using the data obtained by a neutron, density, and PEF logging. The classification results were adjusted by using the micromechanical methods (the Kuster-Toksöz method and the Effective Medium Approximation (EMA)). Examples of the developed methodology application in the carbonate reservoirs are presented.

Interpretable machine learning model for shear wave estimation in a carbonate reservoir using LightGBM and SHAP: a case study in the Amu Darya right bank

The shear wave velocity (Vs) is significant for quantitative seismic interpretation. Although numerous studies have proved the effectiveness of the machine learning method in estimating the Vs using well-logging parameters, the real-world application is still hindered because of the black-box nature of machine learning models. With the rapid development of the interpretable machine learning (ML) technique, the drawback of ML can be overcome by various interpretation methods. This study applies the Light Gradient Boosting Machine (LightGBM) to predict the Vs of a carbonate reservoir and uses the Shapley Additive Explanations (SHAP) to interpret the model. The application of ML in Vs estimation normally involves using conventional well-log data that are highly correlated with Vs to train the model. To expand the model’s applicability in wells that lack essential logs, such as the density and neutron logs, we introduce three geologically important features, temperature, pressure, and formation, into the model. The LightGBM model is tuned by the automatic hyperparameter optimization framework; the result is compared with the Xu-Payne rock physics model and four machine learning models tuned with the same process. The results show that the LightGBM model can fit the training data and provide accurate predictions in the test well. The model outperforms the rock physics model and other ML models in both accuracy and training time. The SHAP analysis provides a detailed explanation of the contribution of each input variable to the model and demonstrates the variation of feature contribution in different reservoir conditions. Moreover, the validity of the LightGBM model is further proved by the consistency of the deduced information from feature dependency with the geological understanding of the carbonate formation. The study demonstrates that the newly added features can effectively improve model performance, and the importance of the input feature is not necessarily related to its correlation with Vs

Spatially resolved Kennicutt–Schmidt relation at z ≈ 7 and its connection with the interstellar medium properties

ABSTRACT We exploit moderately resolved [O $\scriptstyle \rm III$], [C $\scriptstyle \rm II$] and dust continuum ALMA observations to derive the gas density (n), the gas-phase metallicity (Z), and the deviation from the Kennicutt–Schmidt (KS) relation (κs) on $\approx \, \rm sub-kpc$ scales in the interstellar medium (ISM) of five bright Lyman Break Galaxies at the Epoch of Reionization (z ≈ 7). To do so, we use GLAM, a state-of-art, physically motivated Bayesian model that links the [C $\scriptstyle \rm II$]and [O $\scriptstyle \rm III$]surface brightness (Σ[CII], Σ[OIII]) and the SFR surface density (ΣSFR) to n, κs, and Z. All five sources are characterized by a central starbursting region, where the Σgas versus ΣSFR align ≈10 × above the KS relation (κs ≈ 10). This translates into gas depletion times in the range tdep ≈ 80 − 250 Myr. The inner starbursting centres are characterized by higher gas density (log (n/cm−3) ≈ 2.5–3.0) and higher metallicity (log (Z/Z⊙) ≈ −0.5) than the galaxy outskirts. We derive marginally negative radial metallicity gradients (∇log Z ≈ −0.03 ± 0.07 dex/kpc), and a dust temperature (Td ≈ 32 − 38 K) that anticorrelates with the gas depletion time.

Missing well-log reconstruction using a sequence self-attention deep-learning framework

Well logging is a critical tool for reservoir evaluation and fluid identification. However, due to borehole conditions, instrument failure, economic constraints, etc., some types of well logs are occasionally missing or unreliable. Existing logging curve reconstruction methods based on empirical formulas and fully connected deep neural networks (FCDNN) can only consider point-to-point mapping relationships. Recurrently structured neural networks can consider a multipoint correlation, but it is difficult to compute in parallel. To take into account the correlation between log sequences and achieve computational parallelism, we develop a novel deep-learning framework for missing well-log reconstruction based on state-of-the-art transformer architecture. The missing well-log transformer (MWLT) uses a self-attention mechanism instead of a circular recursive structure to model the global dependencies of the inputs and outputs. To use different usage requirements, we design the MWLT in three scales: small, base, and large, by adjusting the parameters in the network. A total of 8609 samples from 209 wells in the Sichuan Basin, China, are used for training and validation, and two additional blind wells are used for testing. The data augmentation strategy with random starting points is implemented to increase the robustness of the model. The results show that our proposed MWLT achieves a significant improvement in accuracy over the conventional Gardner’s equation and data-driven approaches such as FCDNN and bidirectional long short-term memory, on the validation data set and blind test wells. The MWLT-large and MWLT-base have lower prediction errors than MWLT-small but require more training time. Two wells in the Songliao Basin, China, are used to evaluate the cross-regional generalized performance of our method. The generalizability test results demonstrate that density logs reconstructed by MWLT remain the best match to the observed data compared with other methods. The parallelizable MWLT automatically learns the global dependence of the parameters of the subsurface reservoir, enabling an efficient missing well-log reconstruction performance.

Coal Structure Characteristics of the 2# Coal Seam in the Jiaozuo Mining Area and Its Geological Dependence.

To clarify the coal structure, spatial distribution, and controlling factors of the 2# coal seam in Jiaozuo mining, the drilling coal samples were collected to observe the coal type and coal structure. The coal macerals were identified by a MPVSP microscope photometer, and the spatial characteristics of the coal structure were obtained through interpreting deep lateral resistivity logging, natural gamma ray logging, density logging, and acoustic logging curves. The influence of coal properties, burial depth, geological stress, and faults on the coal structure were discussed correspondingly. The results exhibit that granulitic-mylonite coal was most developed in the 2# coal seam, followed by primary coal and cataclastic coal; the coal type was dominated by semibright coal, followed by clarain and semidull coal. Granulitic-mylonite, cataclastic, and primary coals were the main components of clarain, semibright coal, and semidull coal, respectively. Higher vitrinite and organic matter contents were conducive to the development of granulitic-mylonite. The coal structure combinations were spatially varied, and the granulitic-mylonite combinations were the most common. Granulitic-mylonite coal was developed in the east and south parts of the study area, and the coal structure was fragmented with a greater burial depth and larger thickness. The geological stress is the fundamental cause of coal structure damage as well as the cutting of faults.

Identification of Coal Distribution Pattern Using Well Logging Method Based on Gamma Ray Log Data and Log Density in Area X PT PMC Site Sungai Lilin

Identification of Coal Distribution Pattern Using Well Logging Method Based on Gamma Ray Log Data and Log Density in Area X PT PMC Site Sungai Lilin

Petrophysical Properties and Identification of Electrofacies from Well Log Data of Nahr Umr Formation in Subba Oilfield, Southern Iraq

The main purpose of this study is to identify electrofacies and evaluate the petrophysical properties of the Nahr Umr Formation in four wells: A, B, C, and D in the Subba oilfield, southern Iraq. The petrophysical properties, such as shale volume, porosity, water saturation, hydrocarbon saturation, and bulk water volume, were computed and interpreted using Techlog software. According to the interpretation of density, neutron, and gamma ray sonic logs and deep resistivity logs using the IPSOM technique, four electrofacies were identified as sandstone, shaly sand, sandy shale, and shale electrofacies. The formation was divided into three parts based on well log interpretation and petrophysical analysis: the upper part, middle part, and lower part Interpretation of formation lithology and petrophysical parameters shows that the lower part consists mainly sandy shale with layers of sandstone and shale high porosity and high oil saturation whereas middle part consists mainly of sandstone, alternating with thin bedded of sandy shale and shale with high porosity and medium oil saturation. The upper part consists mainly of a sandy shale alternating with shale and shaly sand with high porosity, hydrocarbon saturation, and lower water saturation.

Hydrocarbon Formation Evaluation Using Well Log Data Of Well Tmg-02, Opolo Field, Niger Delta

Well TMG-02 with the depth interval of 5058.77 to 9389.43ft of Opolo field located in the Niger delta was assessed for hydrocarbon using suite of geophysical well logs. Suite includes gamma ray (GR), formation density (RHOB), neutron porosity (NPHI), and resistivity logs. The analysis was carried out to estimate the field’s hydrocarbon prospect by identifying hydrocarbon bearing reservoirs and their properties. The quantitative and qualitative results, identified thicker units of sand than shale lithology, three reservoirs A, B, C within the depth ranges from 5058.77ft to 9389.43ft, capable of accumulating hydrocarbon based on the petrophysical parameters calculated were delineated. The effective porosity for each of the reservoir are: 27%, 24% and 19% respectively. It was observed that reservoir A, B had excellent permeability while reservoir C was low as a result of thicker shale sequence within the reservoir. The result obtained shows presence of hydrocarbon bearing gas water contact in Reservoir A at depth of 5119.70ft, gas oil contact and oil water contact at depths 7310.00ft and 7438.69ft in Reservoir B and Gas water contact at depth 9032.00ft at Reservoir C.

Detection of Over Normal Pore Pressure Intervals by Using Well Logs

Pore pressure is very important parameter that impacting on drilling, production planning and operations. Drilling and production processes cannot be beginning if pore pressure is not estimated. There is a limit of difference between hydrostatic pressure of mud column and pore pressure during drilling to ensure that the layers are preserved from fracturing, as well as that kicking does not occur inside the well. Difference between pore pressure and bottom hole flowing pressure is a key for production process. Over (Abnormal) pressure intervals are causing many problems during drilling. In present study, pore pressure is estimated firstly as a hydrostatic pressure, and secondly, after determination of shale flag, two methods of Eaton slowness and Bowers original are used for detecting of over (Abnormal) pressure shale intervals. Compressional and density logs of three wells (X3, X4, and XD) located at Y oil field and producing from Asmari formation are used to perform the present study. Density log is linearly extrapolated to estimate bulk density from zero depth to last depth point at reservoir. Vertical stress is predicted for these three wells. The vertical stress gradients were 1.03, 0.99, and 0.93 psi/ft for XD, X3, and X4 wells respectively. Results are reveals that Bowers original better than Eaton slowness in detection of over (abnormal) pressure intervals where the last did not cut all shale intervals by compressional slowness shale base line that equal to 80 us/ft so, Eaton slowness method provided either very high over pressure in some shale intervals or subnormal pressure to other shale intervals and that inaccurate while Bowers original method approximately provided all shale intervals as over pressure in reasonable values. Modular dynamic tester measurements for pore pressure of these three wells are used for calibration. Maximum percent error between predicted and measured pore pressure of wells (X3, X4, and XD) are 1.2%, 0.89% and 3% respectively where these percent are very acceptable. Maximum over pressure values in Asmari formation zones at wells are as follows: 6328 psi at depth 3225 m in well X3, 7538 psi at depth 3080 m in well X4, and 6731 psi at true vertical depth 3067 m in well XD.

Neural Network for Porosity Prediction in Carbonate Formation: A Case Study of the South of Iraq

This study presents the application of a Sequence-to-Sequence (Seq2Seq) recurrent neural network for estimating porosity log data by leveraging information from other logs. The effectiveness of this technique in simulating porosity for heterogeneous reservoirs is demonstrated by employing data from the Yamama Formation in the Faihaa Oil field in southern Iraq. Four wells in the field were used for the model training and evaluation, where input data comprised density, neutron, gamma-ray, and porosity logs. The model's performance was assessed using absolute percentage, and root means squared errors, and the results were compared against actual data, revealing a significant correlation. These results establish the Seq2Seq recurrent neural network model as a superior option for predicting reservoir porosity from other good log data. They could have practical implications in estimating porosity for petroleum calculations. .

Secondary porosity and pore aspect ratios integrated with permeability and FMI logs to characterize flow zones at a Port Mayaca aquifer in South Florida

The complex pore structure of carbonate aquifers presents a challenge to interpreters analyzing geophysical logs and geologic data. A significant task is to develop physical rock models to determine the microstructure that provides information about flow-zone paths within the aquifer. In an attempt to achieve this task, an algorithm is devised to predict the secondary porosity formed by stiff macropores, compliant micropores, touching vugs, and pore aspect ratios from sonic logs. The pore aspect ratios are classified in intervals that delineate permeable and low permeability zones of aquifers. This provides information about the presence of isolated vugs, which do not contribute to the flow and connect or touch vugs that are part of the flow zones. The inversion results determined that permeable channels have pore aspect ratios 0.01–0.2. Alternatively, vugs with aspect ratios 0.5–1 are not forming permeable paths because they are isolated and are not contributing to flow zones. The inversion of P- and S-wave velocity logs using density and total porosity logs obtains the secondary porosity and pore aspect ratios. It found optimum correlation coefficients of 0.9975 for S-wave and 0.9405 for P-wave velocities by constraining the solution with the natural relation of the total porosity versus primary plus secondary porosities. The Port Mayaca aquifer includes Stoneley-wave permeability, formation microimager logs, and microresistivity logs, which together with pore aspect ratio and secondary porosity logs delineate and characterize the flow zones. In addition, data integration demonstrates that the vug porosity log detects fractures in resistive, permeable, and dense cemented zones. This new finding creates vug signatures, with their maximums resembling the shapes of the corresponding resistivity heights in the microresistivity log. In conclusion, it is shown that the anomaly zones correspond to water production regions, and their presence is confirmed with flow meter logs.

Penentuan Litologi Batuan di Daerah Samboja Berdasarkan Analisis Pemodelan 3D Data Cutting dan Data Logging Geofisika

The interpretation of rock lithology is inaccurate if it only uses cutting data, so it must be assisted with other data, including logging data. The purpose of this study was to determine the lithological arrangement of rocks based on cutting data and logging data and to compare the cross section results of cutting data and logging data. This study uses cutting data and logging data (gamma ray log and density log). Cutting data and logging data will be interpreted and processed into 2D and 3D sections which are corrected with topographical data including coordinates, elevation values, strike dip and total depth. The results of processing from both methods, obtained lithology composition in the form of soil, silt, sand, coal, clay, carbon and carbont clay.

Interpretasi Data Geophysical Logging untuk Penentuan Sebaran Seam Batubara dalam Bentuk Model 3D

In coal exploration activities, geophysical methods are usually used. One of the most accurate and effective geophysical methods that is still used in coal exploration today is the well logging method. The purpose of this research is to determine the distribution of coal seams in 3D based on well logging data. The data used in this study is secondary data which includes Gamma Ray Log data, Density Log data, and coordinate point data. Gamma Ray Log and Density Log data are interpreted to determine the types of lithology that make up the drill holes, especially coal. Based on the interpretation of the 3D model of the distribution of coal seams in each data it is known that in Seam A it tends to thin to the Southeast and thicken to the North-East, in Seam B it tends to thin out to the Southeast and Northeast, and in Seam C it tends to split to the North-West. and Northeast with a strike ranging from N 53ºE to N 116ºE with a dip ranging from 5º to 12º.

Geological and Engineering ‘Sweet Spots’ in the Permian Lucaogou Formation, Jimusar Sag, Junggar Basin

AbstractUnconventional oil and gas resources require petrophysical logs to answer the question of how best to optimize geological and engineering ‘sweet spots’. Therefore, the establishment of a key well with comprehensive descriptions of lithology, reservoir properties, hydrocarbon‐bearing properties, electronic well log responses, source rock properties, brittleness, and in situ stress magnitude and direction is important for the effective exploration and production of unconventional hydrocarbon resources. Cores, thin sections, scanning electron microscopy (SEM) and comprehensive well log suites are used to build a key well for the Permian Lucaogou Formation, Jimusar Sag of the Junggar Basin. The results show that there are three main types of lithologies, including siltstone, mudstone and dolostone. Lithologies can be predicted using the combination of conventional well and image logs. The pore spaces consist of interparticle pores, intragranular dissolution pores and micropores. Nuclear Magnetic Resonance (NMR) T2 components longer than 1.7 ms are superposed as effective porosity. Permeability is calculated using the Coates model from NMR T2 spectra. The ratio of T2 components >7.0 ms to T2 components >0.3 ms is used to calculate oil saturation. TOC is calculated using the ΔlogR method. Brittleness index is calculated using Poisson‐Young's method, ranging from 13.42%–70.53%. In situ stress direction is determined, and in situ stress magnitudes (maximum horizontal stress SHmax, minimum horizontal stress Shmin, vertical stress Sv) are calculated using density and sonic logs. The strike‐slip stress type (SHmax > Sv > Shmin) is encountered. The key well which comprehensively includes the above seven properties is established. Geological and engineering (geomechanical) ‘sweet spots’ are then optimized from the key well by fully analyzing lithology, reservoir property, oil‐bearing potential, in situ stress magnitude and brittleness. It is hoped that the results support engineers' and geologists' decisions for the future exploitation of unconventional hydrocarbon resources.

Analysis of Petrophysical Parameter on Shaly Sand Reservoir by Comparing Conventional Method and Shaly Sand Method in Vulcan Subbasin, Northwest Australia

Vulcan Subbasin is an area with a lot of oil and gas exploration where is located in the Bonaparte Basin, Northwest Australia. There is some formation identified as sandstone reservoir with clay content which is usually called shaly sand based on the screening between resistivity log and density log. Clay content caused lower resistivity log readings so the shaly sand reservoir is considered as non-reservoir. To overcome this, a method besides the conventional method was applied to analyze the petrophysical parameters of shaly sand reservoir, it was shaly sand method. Petrophysical analysis is an analysis of rock physical parameters such as shale volume, porosity, and water saturation based on well log data. In this study, petrophysical analysis was carried out in the Vulcan Subbasin using 35 well log data, including gamma ray log, resistivity log, neutron log, and density log for the conventional method and shaly sand method involved Stieber equation and Thomas Stieber plot. The results obtained from this study are the comparison of petrophysical parameter values and pay summary between the conventional method and the shaly sand method, also its relation to the shale distribution type. By applying the shaly sand method, the average shale volume has decreased, the average porosity has increased, the average water saturation has increased, the average net to gross has increased, the average net thickness has increased, and the average net pay has increased. Changes in the average value were caused by laminated-dispersed shale distribution type which is influenced by diagenesis and the depositional environment of the formation.

An integrated density correction method of four-detector density logging in cased holes

Density logging is critical to shale oil and gas exploration. In density logging of cased holes, the inelastic gamma count and hydrogen index are closely related to the correction of formation density. Aimed at the problems of obtaining the pure inelastic gamma count and correcting the hydrogen index in density logging, the relevant research is implemented in the four detector logging tool. Firstly, a technique to separate the pure inelastic gamma count for density logging is provided. It is based on the pure count coefficient. Then on the basis of determining the density through the pure inelastic count ratio, the hydrogen index is corrected by the epithermal neutron and thermal neutron count ratio. Finally, the integrated density correction equation is proposed. The accuracy of this method is checked using a Monte Carlo model, which provides a strong experimental foundation. By examining the influencing factors, the results show that while the density after Halliburton correction is significantly impacted by borehole size, lithology, salinity, and borehole fluid, the density after integrated correction is least affected by these variables. Besides, the integrated density correction method is more favorable for large borehole size, low salinity and sandstone formation. Through logging examples, it is discovered that the overall uncertainty of the density log after the integrated density correction is lower than the Halliburton density correction log, with a maximum of only 0.013 g/cm3, indicating that the density results are more reliable. The study's findings offer a correction technique for precisely estimating the density of cased holes.

A Method for Judging the Effectiveness of Complex Tight Gas Reservoirs Based on Geophysical Logging Data and Using the L Block of the Ordos Basin as a Case Study

As an important unconventional oil and gas resource, the tight gas reservoir faces many technical challenges due to its low porosity, low permeability, and strong heterogeneity. Among them, the accurate definition of effective reservoirs and ineffective reservoirs in tight gas reservoirs directly affects the formulation and adjustment of subsequent development plans. This paper proposes a reservoir effectiveness identification method based on double factors based on conventional geophysical logging data and core experimental data. The double factors considered are based on the logging response and physical parameters of the reservoir. The identification factor F1 is obtained by using the difference in the logging response values of the natural gamma logging curve, compensated density logging curve, and acoustic time difference logging curve in different reservoirs combined with mathematical operation, and the identification factor F2 is calculated by using porosity parameters combined with Archie’s formula. The validity of the reservoir can be judged by the intersection of the above double factors. This method is applied to the Shihezi Formation in the L block, and the applicability of the double factors is compared and analyzed using the traditional method. The results show that the method has strong applicability in tight gas reservoirs and that the accuracy rate reaches 96%. Compared with the direct use of the porosity lower limit method, the accuracy of the judgment is significantly improved, and the calculation is simple, easy to implement, and unaffected by mud invasion. For study areas with different geological backgrounds, the process of this method can also be used to determine the effectiveness of the reservoir. The reservoir effectiveness identification method proposed in this paper has practical engineering significance and lays a solid foundation for subsequent fluid property identification, production calculation, and development plan formulation and adjustment.

Estimation of multistage hydraulic fracturing parameters using 4D simulation

At the present stage, most oil and gas condensate fields in the southern part of the East Siberian oil and gas province are characterized by an increasing proportion of difficult oil reserves in tight reservoirs. Multistage hydraulic fracturing (MHF) is proposed for the offshore Challenger Sea field (Southeast Dome). The implementation of this technique at a shelf will be a source of additional risks. For example, the properties of the RR-2 overlying seal have not been unambiguously assessed, and there are a number of geological uncertainties, such as the tectonic regime. However, there are a number of arguments in favor of MHF: heterogeneity of the reservoir; low permeability; low water cut of the field; sufficient thickness of the pay zone; and the overlying seal. One more positive factor is that sand ingress is not observed in the process of oil production. The selection of a principal well completion scheme on the eastern side of the RR-7 formation is aimed at effectively recovering the remaining reserves. The objectives of the study performed are: to create a geological and hydrodynamic model of the Challenger Sea (Southeast Dome); develop 1D and 3D geomechanical models; evaluate oil production forecasts based on fundamentally different well completion schemes; and determine the optimum parameters for multistage hydraulic fracturing. The research methods included: petrophysical methods; logging methods; core studies; drilling reports and formation testing data; and 3D, 4D geomechanical simulation. Other geophysical methods included acoustic logging, density logging, and gamma-ray logging. After building a geomechanical model of the reservoir at the beginning of drilling, a hydrodynamic calculation was performed. This established the reservoir pressures and saturations at certain points in time. The results made it possible for the principal stress directions, the values of effective and principal stresses, and the values of elastic strains to be determined. In order to assess MGF process efficiency, production forecasts were made using a hydrodynamic model for an exploration well with conventional completion (perforated liner) and with five-stage MGF. In the first case, the accumulated production was 144 kt over 15 years, and in the second case, 125 kt over 17 years. The difference in cumulative production is due to different initial well flow rates, as well as the rate of oil withdrawal during the first few years of development. Thereafter, the production and daily flow rate curves showed similar behavior. In order to select the most effective option, an economic analysis of the efficiency was performed.