scholarly journals Reservoir properties estimation from 3D seismic data in the Alose field using artificial intelligence

2021 ◽  
Vol 11 (3) ◽  
pp. 1275-1287
Author(s):  
A. Ogbamikhumi ◽  
J. O. Ebeniro
Keyword(s):  
Neural Network ◽  
Correlation Coefficient ◽  
Feasibility Study ◽  
Cross Validation ◽  
Water Saturation ◽  
Seismic Inversion ◽  
Reservoir Properties ◽  
Before And After ◽  
Secondary Constraint ◽  
Static Modeling

AbstractIn an attempt to reduce the errors and uncertainties associated with predicting reservoir properties for static modeling, seismic inversion was integrated with artificial neural network for improved porosity and water saturation prediction in the undrilled prospective area of the study field, where hydrocarbon presence had been confirmed. Two supervised neural network techniques (MLFN and PNN) were adopted in the feasibility study performed to predict reservoir properties, using P-impedance volumes generated from model-based inversion process as the major secondary constraint parameter. Results of the feasibility study for predicted porosity with PNN gave a better result than MLFN, when correlated with well porosity, with a correlation coefficient of 0.96 and 0.69, respectively. Validation of the prediction revealed a cross-validation correlation of 0.88 and 0.26, respectively, for both techniques, when a random transfer function derived from a given well is applied on other well locations. Prediction of water saturation using PNN also gave a better result than MLFN with correlation coefficient of 0.97 and 0.57 and cross-validation correlation coefficient of 0.89 and 0.3, respectively. Hence, PNN technique was adopted to predict both reservoir properties in the field. The porosity and water saturation predicted from seismic in the prospective area were 24–30% and 20–30%, respectively. This indicates the presence of good quality hydrocarbon bearing sand within the prospective region of the studied reservoir. As such, the results from the integrated techniques can be relied upon to predict and populate static models with very good representative subsurface reservoir properties for reserves estimation before and after drilling wells in the prospective zone of reservoirs.

Integrated workflow approach to static modeling of Igloo R3 reservoir, onshore Niger Delta, Nigeria

2015 ◽  
Vol 3 (3) ◽  
pp. SZ1-SZ14 ◽  
Author(s):  
Emmanuel Kenechukwu Anakwuba ◽  
Clement Udenna Onyekwelu ◽  
Augustine Ifeanyi Chinwuko
Keyword(s):  
Correlation Coefficient ◽  
Acoustic Impedance ◽  
Niger Delta ◽  
Seismic Inversion ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Sequential Gaussian Simulation ◽  
Reservoir Properties ◽  
Static Modeling ◽  
Geophysical Well Logs

We constructed a 3D static model of the R3 reservoir at the Igloo Field, Onshore Niger Delta, by integrating the 3D seismic volume, geophysical well logs, and core petrophysical data. In this model, we used a combined petrophysical-based reservoir zonation and geostatistical inversion of seismic attributes to reduce vertical upscaling problems and improve the estimation of reservoir properties between wells. The reservoir structural framework was interpreted to consist of three major synthetic faults; two of them formed northern and southern boundaries of the field, whereas the other one separated the field into two hydrocarbon compartments. These compartments were pillar gridded into 39,396 cells using a [Formula: see text] dimension over an area of [Formula: see text]. Analysis of the field petrophysical distribution showed an average of 21% porosity, 34% volume of shale, and 680-mD permeability. Eleven flow units delineated from a stratigraphic modified Lorenz plot were used to define the reservoir’s stratigraphic framework. The calibration of acoustic impedance using sonic- and density-log porosity showed a 0.88 correlation coefficient; this formed the basis for the geostatistic seismic inversion process. The acoustic impedance was transformed into reservoir parameters using a sequential Gaussian simulation algorithm with collocated cokriging and variogram models. Ten equiprobable acoustic impedance models were generated and further converted into porosity models by using their bivariate relationship. We modeled the permeability with a single transform of core porosity with a correlation coefficient of 0.86. We compared an alternative model of porosity without seismic as a secondary control, and the result showed differences in their spatial distributions, which was a major control to fluid flow. However, there were similarities in their probability distribution functions and cumulative distribution functions.

Predicting the Productivity Enhancement After Applying Acid Fracturing Treatments in Naturally Fractured Reservoirs Utilizing Artificial Neural Network

2021 ◽  
Author(s):  
Amjed Mohamed Hassan ◽  
Murtada Saleh Aljawad ◽  
Mohamed Ahmed Mahmoud
Keyword(s):  
Neural Network ◽  
Correlation Coefficient ◽  
Fractured Reservoirs ◽  
Naturally Fractured Reservoirs ◽  
Percentage Error ◽  
Reservoir Properties ◽  
Productivity Improvement ◽  
Acid Fracturing ◽  
New Correlation ◽  
Naturally Fractured

Abstract Acid fracturing treatments are conducted to increase the productivity of naturally fractured reservoirs. The treatment performance depends on several parameters such as reservoir properties and treatment conditions. Different approaches are available to estimate the efficacy of acid fracturing stimulations. However, a limited number of models were developed considering the presence of natural fractures (NFs) in the hydrocarbon reservoirs. This work aims to develop an efficient model to estimate the effectiveness of acid fracturing treatment in naturally fractured reservoirs utilizing an artificial neural network (ANN) technique. In this study, the improvement in hydrocarbon productivity due to applying acid fracturing treatment is estimated, and the interactions between the natural fractures and the induced ones are considered. More than 3000 scenarios of reservoir properties and treatment parameters were used to build and validate the ANN model. The developed model considers reservoir and treatment parameters such as formation permeability, injection rate, natural fracture spacing, and treatment volume. Furthermore, percentage error and correlation coefficient were determined to assess the model prediction performance. The proposed model shows very effective performance in predicting the performance of acid fracturing treatments. A percentage error of 6.3 % and a correlation coefficient of 0.94 were obtained for the testing datasets. Furthermore, a new correlation was developed based on the optimized AI model. The developed correlation provides an accurate and quick prediction for productivity improvement. Validation data were used to evaluate the reliability of the new equation, where a 6.8% average absolute error and 0.93 correlation coefficient were achieved, indicating the high reliability of the proposed correlation. The novelty of this work is developing a robust and reliable model for predicting the productivity improvement for acid fracturing treatment in naturally fractured reservoirs. The new correlation can be utilized in improving the treatment design for naturally fractured reservoirs by providing quick and reliable estimations.

Bayesian seismic AVO inversion for reservoir variables with bimodal spatial histograms

Geophysics ◽  
2021 ◽  
pp. 1-141
Author(s):  
Ole Bernhard Forberg ◽  
Øyvind Kjøsnes ◽  
Henning Omre
Keyword(s):  
Random Field ◽  
Linear Models ◽  
Water Saturation ◽  
Gaussian Random Field ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Oil Reservoir ◽  
Initial State ◽  
Reservoir Properties ◽  
Avo Inversion

We consider seismic AVO inversion for prediction of the reservoir properties porosity and water saturation. An oil reservoir at initial state is studied; hence gravitational effects dominate and keep hydrocarbons from mixing with water. Histograms of observations of water saturation along wells are consequently clearly bimodal, which is challenging to model. The seismic AVO inversion is cast in a Bayesian framework. The prior spatial model for porosity and water saturation is specified to be a selection Gaussian random field, which is capable of representing spatial variables with multimodal histograms. By using linear models for the seismic and rock-physics likelihoods, the posterior model is also a selection Gaussian random field. Hence, the Bayesian seismic inversion can be solved analytically and the bimodal characteristics of the water saturations can be reproduced. The methodology is defined and demonstrated on two synthetic cases inspired by real data from an oil reservoir. Compared to standard spatial Gaussian models, the improvement of the inversion results is substantial. Inversion of the real seismic AVO data along a well trace reproduces the corresponding well observations fairly precisely, and is considered very encouraging.

Integrated reservoir characterization of a Utica Shale with focus on sweet spot discrimination

2020 ◽  
Vol 8 (3) ◽  
pp. SM1-SM14
Author(s):  
Jinming Zhu
Keyword(s):  
Reservoir Characterization ◽  
Water Saturation ◽  
Clay Content ◽  
Seismic Inversion ◽  
Reservoir Properties ◽  
Geomechanical Properties ◽  
Utica Shale ◽  
Characterization Study ◽  
Separate Point

Multiclient 3D seismic data were acquired in 2015 in eastern Ohio for reservoir characterization of the Utica Shale consisting of the Utica and Point Pleasant Formations. I attained accurate, high-fidelity acoustic impedance, shear impedance, density, and [Formula: see text], from elastic inversion. These accurate inversion results allow consistent calculation of reservoir and geomechanical properties of the Utica Shale. I found density critically important affecting the accuracy of other reservoir and geomechanical properties. More than a dozen properties in geologic, geomechanical, and reservoir categories were acquired from logs, cores, and seismic inversion, for this integrated reservoir characterization study. These properties include buried depth, formation thickness, mineralogy, density, Young’s modulus, Poisson’s ratio (PR), brittleness, total organic carbon (TOC), porosity, water saturation, permeability, clay content, and natural fractures. A ternary diagram of core samples from 18 wells demonstrates that the Point Pleasant is dominant with calcite, whereas the Utica mainly contains clay. Inverted density clearly divides Point Pleasant as low density from the overlying Utica. Calculated reservoir properties undoubtedly delineate the traditional Utica Shale as two distinctive formations. I calculated that the Utica Formation contains 1%–2% TOC, 3.5%–4.8% porosity, 10%–24% water saturation, and 40%–58% clay content, whereas Point Pleasant contains 3%–4.5% TOC, 5%–9% porosity, 2%–10% water saturation, and 15%–35% clay content. The PR and brittleness clearly separate Point Pleasant from the overlying Utica, with a lower PR and a higher brittleness index in Point Pleasant than in Utica. The higher brittleness in Point Pleasant makes it easier to frac, leading to enhanced permeability. Both formations exhibit spatial variations of reservoir and geomechanical properties. Nevertheless, the underlying Point Pleasant is obviously better than the Utica Shale with favorable reservoir and geomechanical properties for optimal development and production, although Utica is thicker and shallower. The central and southeastern portions of Point Pleasant have the sweetest reservoirs.

scholarly journals Hydrocarbon Prospectivity in the undrilled area of AIMA Field in the Niger Delta Basin, Nigeria.

2021 ◽  
Vol 6 (1) ◽  
pp. 34-41
Author(s):  
Alexander Ogbamikhumi ◽  
John Elvis Ighodalo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Niger Delta ◽  
Water Saturation ◽  
Poisson Ratio ◽  
Rock Physics ◽  
Reservoir Properties ◽  
Field Development ◽  
Seismic Reservoir ◽  
Niger Delta Basin

Field development is a very costly endeavor that requires drilling several wells in an attempt to understanding potential prospects. To help reduce the associated cost, this study integrates well and seismic based rock physics analysis with artificial neural network to evaluation identified prospects in the field.  Results of structural and amplitude maps of three major reservoir levels revealed structural highs typical of roll over anticlines with amplitude expression that conforms to structure at the exploited zone where production is currently ongoing. Across the bounding fault to the prospective zones, only the D_2 reservoir possessed the desired amplitude expression, typical of hydrocarbon presence. To validate the observed amplitude expression at the prospective zone, well and seismic based rock physics analyses were performed. Results from the analysis presented Poisson ratio, Lambda-Rho and Lambda/Mu-Rho ratio as good fluid indicator while Mu-Rho was the preferred lithology indicator.  These rock physics attributes were employed to validate the observed prospective direct hydrocarbon indicator  expressions on seismic. Reservoir properties maps generated for porosity and water saturation prediction using Probability Neural Network gave values of 20-30% and 25-35% for water saturation and porosity respectively, indicating  the presence of good quality hydrocarbon bearing reservoir at the prospective zone.

133Xe-DSPECT: Normalwerte von zerebraler Ruhedurchblutung und Reservekapazität

1987 ◽  
Vol 26 (05) ◽  
pp. 192-197 ◽  
Author(s):  
T. Kreisig ◽  
P. Schmiedek ◽  
G. Leinsinger ◽  
K. Einhäupl ◽  
E. Moser
Keyword(s):  
Blood Flow ◽  
Cerebral Blood Flow ◽  
Cerebrovascular Disease ◽  
Correlation Coefficient ◽  
Regional Cerebral Blood Flow ◽  
Regression Line ◽  
Reserve Capacity ◽  
Regional Cerebral Blood ◽  
Quantitative Measurements ◽  
Before And After

Using the 133Xe-DSPECT technique, quantitative measurements of regional cerebral blood flow (rCBF) were performed before and after provocation with acetazolamide (Diamox) i. v. in 32 patients without evidence of brain disease (normals). In 6 cases, additional studies were carried out to establish the time of maximal rCBF increase which was found to be approximately 15 min p. i. 1 g of Diamox increases the rCBF from 58 ±8 at rest to 73±5 ml/100 g/min. A Diamox dose of 2 g (9 cases) causes no further rCBF increase. After plotting the rCBF before provocation (rCBFR) and the Diamox-induced rCBF increase (reserve capacity, Δ rCBF) the regression line was Δ rCBF = −0,6 x rCBFR +50 (correlation coefficient: r = −0,77). In normals with relatively low rCBF values at rest, Diamox increases the reserve capacity much more than in normals with high rCBF values before provocation. It can be expected that this concept of measuring rCBF at rest and the reserve capacity will increase the sensitivity of distinguishing patients with reversible cerebrovascular disease (even bilateral) from normals.

Oil Saturation Log Prediction Using Neural Network in New Steamflood Area

2020 ◽  
Author(s):  
A. Syahputra
Keyword(s):  
Neural Network ◽  
Water Saturation ◽  
Prediction Method ◽  
Neural Model ◽  
Training Model ◽  
Acquisition Time ◽  
Oil Saturation ◽  
Full Field ◽  
Remaining Oil ◽  
Observation Wells

Surveillance is very important in managing a steamflood project. On the current surveillance plan, Temperature and steam ID logs are acquired on observation wells at least every year while CO log (oil saturation log or SO log) every 3 years. Based on those surveillance logs, a dynamic full field reservoir model is updated quarterly. Typically, a high depletion rate happens in a new steamflood area as a function of drainage activities and steamflood injection. Due to different acquisition time, there is a possibility of misalignment or information gaps between remaining oil maps (ie: net pay, average oil saturation or hydrocarbon pore thickness map) with steam chest map, for example a case of high remaining oil on high steam saturation interval. The methodology that is used to predict oil saturation log is neural network. In this neural network method, open hole observation wells logs (static reservoir log) such as vshale, porosity, water saturation effective, and pay non pay interval), dynamic reservoir logs as temperature, steam saturation, oil saturation, and acquisition time are used as input. A study case of a new steamflood area with 16 patterns of single reservoir target used 6 active observation wells and 15 complete logs sets (temperature, steam ID, and CO log), 19 incomplete logs sets (only temperature and steam ID) since 2014 to 2019. Those data were divided as follows ~80% of completed log set data for neural network training model and ~20% of completed log set data for testing the model. As the result of neural model testing, R2 is score 0.86 with RMS 5% oil saturation. In this testing step, oil saturation log prediction is compared to actual data. Only minor data that shows different oil saturation value and overall shape of oil saturation logs are match. This neural network model is then used for oil saturation log prediction in 19 incomplete log set. The oil saturation log prediction method can fill the gap of data to better describe the depletion process in a new steamflood area. This method also helps to align steam map and remaining oil to support reservoir management in a steamflood project.

Deep fluids and their role in hydrocarbon migration and oil deposit formation exemplified by supercritical СO2

2021 ◽  
pp. 1-11
Author(s):  
Sara LIFSHITS
Keyword(s):  
Supercritical Fluid ◽  
Oil And Gas ◽  
Gas Permeability ◽  
Sedimentary Rocks ◽  
Source Rocks ◽  
Reservoir Properties ◽  
Hydrocarbon Migration ◽  
Geological Processes ◽  
Before And After ◽  
Oil Source

ABSTRACT Hydrocarbon migration mechanism into a reservoir is one of the most controversial in oil and gas geology. The research aimed to study the effect of supercritical carbon dioxide (СО2) on the permeability of sedimentary rocks (carbonates, argillite, oil shale), which was assessed by the yield of chloroform extracts and gas permeability (carbonate, argillite) before and after the treatment of rocks with supercritical СО2. An increase in the permeability of dense potentially oil-source rocks has been noted, which is explained by the dissolution of carbonates to bicarbonates due to the high chemical activity of supercritical СО2 and water dissolved in it. Similarly, in geological processes, the introduction of deep supercritical fluid into sedimentary rocks can increase the permeability and, possibly, the porosity of rocks, which will facilitate the primary migration of hydrocarbons and improve the reservoir properties of the rocks. The considered mechanism of hydrocarbon migration in the flow of deep supercritical fluid makes it possible to revise the time and duration of the formation of gas–oil deposits decreasingly, as well as to explain features in the formation of various sources of hydrocarbons and observed inflow of oil into operating and exhausted wells.

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