Identification of Geo – Hazard Using Pore Pressure Analysis in ‘MAC’ Field, Niger Delta

Identification of geo-hazard zones using pore pressure analysis in ‘MAC’ field was carried out in this research. Suite of wireline logs from four wells and RFT pressure data from two wells were utilized. Lithologic identification was done using gamma ray log. Resistivity log was used to delineate hydrocarbon and non-hydrocarbon formations. Well log correlation helps to see the lateral continuity of the sands. Pore pressure prediction was done using integrated approaches. The general lithology identified is alternation of sand and shale units. The stratigraphy is typical of Agbada Formation. Three reservoirs delineated were laterally correlated. Crossplot of Vp against density (Rho) colour coded with depth revealed that disequilibrium compaction is the main overpressure generating mechanism in the field. Prediction of overpressure by normal compaction trend was generated and plot of interval transit time against depth show that there is normal compaction from 250m to about 1700 m on MAC-01, but at a depth of about 1800m, there was abnormal pressure build up that shows the onset of overpressure. A relatively normal compaction was observed on MAC-02 until a depth of about 2100m where overpressure was suspected. The prediction of formation pore pressure using Eaton’s and Bower’s method to determine the better of the two methods to adopt for pore pressure prediction shows that the pore pressure prediction using Eaton’s method gave a better result similar to the acquired pressure in the field. Hence Eaton’s method appears to be better suited for formation pore pressure estimation in ‘MAC’ field. The validation of the pore pressure analysis results with available acquired pressure data affirmed the confidence in the interpreted results for this study.

Application of GSTAR(1,1) model for layer peat soil predicted based on resistivity log data

In this study, GSTAR modeling was carried out with the inverse of distance weight matrix obtained from Geoelectrical Resistivity data at several peatland locations around the Universitas Tanjungpura, Pontianak. This data can identify the subsurface layer of the soil through the electric current that binds into the soil. However, due to the limitation of the tool to measure the resistivity value, it can only measure 1/5 of the depth of the observation length. To overcome this problem, predictions are made at the next depth using the GSTAR model. The study began by measuring the resistivity value of the land using the geoelectric method and mapping it. Through this GSTAR modeling, predictions are made for the unobserved subsurface to determine the type of soil layer. Knowing the type of deeper soil layer can help contractors build plant concrete stakes to keep buildings safe on peatland. The results of the GSTAR(1.1) model are not accurate enough to estimate the resistivity value data. This is possible because the correlation between rock ages is not the same, so further analysis is required.

Vitrinite Equivalent Reflectance Estimation from Improved Maturity Indicator and Well Logs Based on Statistical Methods

Estimation and correct determination of vitrinite equivalent reflectance in rock is crucial for the assessment of the source rock in both conventional and unconventional hydrocarbon deposits. These parameters can be determined in laboratories on rock samples. Laboratory measurements provide only point information. However, the use of well logs could overcome discontinuities in the data and provide parameters throughout a study interval. Attention has been paid to the estimation of TOC based on well logs. Vitrinite equivalent reflectance estimation is less well discussed and most papers reported cases with high TOC content in analyzed deposits. In this paper, the estimation of improved Ro is presented using a calculated maturity indicator with well logs. As the organic matter content is not high, additional steps were required for the calculation. To improve the quality of the fit and to find similar intervals, the data were grouped using cluster and neural network analysis. The next step was to use the resistivity log to improve the obtained maturity indicator. Due to the changing properties of kerogen with the type and degree of thermal maturity, this approach turned out to be reliable. The use of resistivity significantly increased the correlation coefficient and reduced errors. The method was tested on two wells with different type and maturity of kerogen. The obtained results are satisfactory, which makes it possible to use the method even in formations with a low organic matter content.

Total Organic Carbon (TOC) Value Prediction in Source Rock Potential at North East Java Basin, Indonesia

This research aims to determine the potential of the source rock in the Kujung and Cepu Formations in the North East Java Basin, using Total Organic Carbon (TOC). TOC is calculated using the Passey method. The Passey method is used by overlaying the sonic log and the resistivity log and determining the baseline to get the separation of Δlog resistivity, which is then used to predict the TOC log by including the LOM (Level of Organic Maturity) variable obtained from the data of vitrinite reflectance. After the TOC log value is obtained, a correlation is made with the TOC core value. The prediction result of TOC log in a PM-1 well is 2.16%, which means it has excellent quality. The prediction of TOC log in a PM-2 well is worth 2.68%, which means it has excellent quality. The correlation value between the TOC log and the TOC core of the PM-1 well is 0.67, which means the correlation is strong. In PM-2 well, the correlation between the TOC log and TOC core is 0.92, which means that the correlation is robust.

Comprehensive Fracture Characterization in Tight Carbonate Reservoir Using LWD High-Resolution Image and Multi-Pole Sonic Measurement; A Case Study from Offshore North West Java, Indonesia

Tapping into an unconventional reservoir such as naturally fractured tight carbonate or basement has become more common in the industry. Open natural fractures, when present are the major contributor to production flow in such formation. Therefore, a comprehensive understanding of fracture properties including aperture, intensity, and permeability is required to identify the productive fractures and optimize production. In this paper, we discuss the first application of the latest Logging-While Drilling (LWD) high-resolution laterolog resistivity image in combination with LWD multi-pole sonic to provide comprehensive fracture characterization in Pre-Talang Akar Formation tight carbonate reservoir, in the offshore North West Java Basin, Indonesia. The methodology involved identification of borehole breakouts, natural or drilling-induced fractures, faults and vugs from the high-resolution LWD image data, which were then interpreted further to provide the fracture attributes and the secondary porosity distributions from each of the identified features. The Stoneley measurement from LWD multi-pole sonic log enabled the analysis of the fracture system producibility using the sonic fracture technique. The characterization of fractures and faults (open/closed) from the integration of these two independent methods were complemented by the triple combo measurements, caliper, and drilling loss data, as well as sonic compressional and shear data. This methodology has successfully managed to differentiate open fracture zones and closed fracture zones along with their computed fracture properties. The open fracture zones were characterized by a cluster of conductive fractures with large fracture aperture and fracture porosity value. These fractures were also associated with positive fracture indication from the sonic data, decrease in density logs, shallow - deep resistivity log separation and drilling loss occurrence. Whereas, closed fracture zones were characterized with minor fracture dip development. It also showed negative open fracture indication from sonic data, flat density log response and overlaying resistivity log response with no drilling loss occurrence. The case study in this paper shows excellent LWD data quality and fracture characterization result, on par with wireline conveyed data that were commonly used to quantify fracture attributes. The results provide invaluable information for volumetric calculation, well completion and production planning in this area.

Unravelling the 'sweet spot' potential of fractured basement in Ketaling High by integrating complex seismic attribute and resistivity log evaluation.

A proven Pre-Tertiary basement reservoir along NE-SW Ketaling high trend were already known throughout well test in several wells more than 15 years ago, but lack of interest in basement fractures gas on those past years and limited data provided, locked a potential gas accumulation in fractures. The most recently added data from 3D Seismic data and re-evaluation of log data, could seized those prospect. Combination of structural smoothing towards edge - detecting attributes are conducted, preceding ant track attribute generation as initial input for multi scale fault extraction. The following result is fracture distribution depicting area with intensive fracture location. Fracture intensity cube requires validation from well data. With no core data, slightly indication from drilling data, and only basic log available, resistivity log is a reliable option to be used. Log evaluation technique using response analysis of two resistivity by Rasmus (1982) is performed to identify fracture and fluid content in the basement reservoir, where the higher ratio between LLD/LLS indicate higher fracture intensity. Based on log evaluation method applied in K-1 well, the interval with massive fracture development is identified in metamorphic basement interval. The outcome coincide with the interval which later proven to produce gas higher than 1 MMscfd from production test result. Similarly, fracture intensity distribution from complex seismic attribute, ratified the conclusion from the respective method as well. To test the robustness of this method, identical workflow is assigned on other adjacent well penetrating basement interval, where no indication of hydrocarbon existence. Less intensive fracture interval is concluded occur on the respective well location, which responsible of no accumulation of hydrocarbon on the basement reservoir. The proposed workflow and method can be a solution for overlooked fracture basement reservoir optimization analysis due to limited available subsurface data condition.

Rock physics diagnostic of Eocene Sokor-1 reservoir in Termit subbasin, Niger

Eocene Sokor-1 reservoir is intrinsically heterogeneous and characterized by low-contrast low-resistivity log responses in parts of the Termit subbasin. Discriminating lithology and fluid properties using petrophysics alone is complicated and undermines reservoir characterization. Petrophysics and rock physics were integrated through rock physics diagnostics (RPDs) modeling for detailed description of the reservoir microstructure and quality in the subbasin. Petrophysical evaluation shows that Sokor-1 sand_5 interval has good petrophysical properties across wells and prolific in hydrocarbons. RPD analysis revealed that this sand interval could be best described by the constant cement sand model in wells_2, _3, _5 and _9 and friable sand model in well_4. The matrix structure varied mostly from clean and well-sorted unconsolidated sands as well as consolidated and cemented sandstones to deteriorating and poorly sorted shaly sands and shales/mudstones. The rock physics template built based on the constant cement sand model for representative well_2 diagnosed hydrocarbon bearing sands with low Vp/Vs and medium-to-high impedance signatures. Brine shaly sands and shales/mudstones were diagnosed with moderate Vp/Vs and medium-to-high impedance and high Vp/Vs and medium impedance, respectively. These results reveal that hydrocarbon sands and brine shaly sands cannot be distinctively discriminated by the impedance property, since they exhibit similar impedance characteristics. However, hydrocarbon sands, brine shaly sands and shales/mudstones were completely discriminated by characteristic Vp/Vs property. These results demonstrate the robust application of rock physics diagnostic modeling in quantitative reservoir characterization and may be quite useful in undrilled locations in the subbasin and fields with similar geologic settings.

Prediction of Water Saturation from Well Log Data by Machine Learning Algorithms: Boosting and Super Learner

Intelligent predictive methods have the power to reliably estimate water saturation (Sw) compared to conventional experimental methods commonly performed by petrphysicists. However, due to nonlinearity and uncertainty in the data set, the prediction might not be accurate. There exist new machine learning (ML) algorithms such as gradient boosting techniques that have shown significant success in other disciplines yet have not been examined for Sw prediction or other reservoir or rock properties in the petroleum industry. To bridge the literature gap, in this study, for the first time, a total of five ML code programs that belong to the family of Super Learner along with boosting algorithms: XGBoost, LightGBM, CatBoost, AdaBoost, are developed to predict water saturation without relying on the resistivity log data. This is important since conventional methods of water saturation prediction that rely on resistivity log can become problematic in particular formations such as shale or tight carbonates. Thus, to do so, two datasets were constructed by collecting several types of well logs (Gamma, density, neutron, sonic, PEF, and without PEF) to evaluate the robustness and accuracy of the models by comparing the results with laboratory-measured data. It was found that Super Learner and XGBoost produced the highest accurate output (R2: 0.999 and 0.993, respectively), and with considerable distance, Catboost and LightGBM were ranked third and fourth, respectively. Ultimately, both XGBoost and Super Learner produced negligible errors but the latest is considered as the best amongst all.

Interpretation of 1D-Resistivity Data to Describe the Aquifer Model in the Serayu Watershed Area of Somagede Village, Somagede District, Banyumas Regency

Acquisition of resistivity data using the Schlumberger configuration has been carried out in the Serayu watershed area of Somagede Village, Somagede District, Banyumas Regency. The purpose of this research was to describe a groundwater aquifer model based on the interpretation of 1D-resistivity data. The research results are resistivity logs of subsurface rock distributed over seven sounding points with resistivity values ranging from 2.24-192.78 m. The sounding points are located at positions of 7°31′28.55″ and 109°19′8.65″ (Sch-1) to 7°31′18.79″ and 109°19′21.45″ (Sch-7). The interpretation of the resistivity logs has resulted in a lithology log at each sounding point. Based on the interpretation, the lithology of the research area is composed of topsoil (42.85-85.13 m), sandy clay which partly slightly wet (7.08-17.18m), sandy clay inserted with gravel (22.44-31.70 m), sand, gravel, and pebble, with various consolidated (22.16-192.78m), sand inserted by gravel (6.77m), alternating sandstone and claystone, some of which are alternated with marl and tuff (8.71-21.99m), and sandstones with various porosity (3.25-8.76m). Shallow aquifers are interpreted to exist in sand inserted by gravel layer (13.23-27.67 m) at the sounding point of Sch-2 where the potential is quite good. While deep aquifers are estimated to be present in the sandstone layer with various porosity (> 46.67 m) at all sounding points with very good potential.Keywords: 1D-resistivity, Serayu watershed, resistivity log, aquifer, Somagede Village.

Sand injectite mapping using a resistivity-velocity transform function

Lack of resolution in the distribution of sand injectites in hydrocarbon fields is common and makes it difficult to predict drilling challenges and plan for optimum production. A practical workflow was developed that enables the distinction of shale and sand bodies by using a combination of low-resolution seismic data and high-resolution resistivity log data. Measured resistivity logs were used to predict synthetic velocity logs, which accurately match shale velocities and over- or underestimate velocities of other rock types. The synthetic velocity logs were spatially distributed in a 3D cube in order to predict synthetic velocities in between and away from the well locations. The 3D cube was representative of a field. It covered the interval from the seabed to below the reservoir. The spatial distribution was based on a geostatistical approach guided by measured seismic interval velocities. A residual velocity cube was calculated from the measured and synthetic velocities. The residual velocity cube produced near-zero velocities for shaly materials and velocity over- or underestimates for other rock types. Interpretation of the residual velocity cube required the identification of strong stratigraphic markers. The markers were removed from the residual cube by setting their specific layer velocities to 0 m/s. The final information stored in the residual velocity cube was then related to the over- or underestimated velocities in sand bodies.