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East Natuna is well known for its huge natural gas reserves with a very high CO2 content. The appearance of CO2 content in an oil and gas field is always considered as waste material and will severely affect the economic value of the field. The higher the content, the more costly the process, both technically and environmentally. In this research, the newly proposed reservoir management approach called CSSU (Carbon Sequestration Storage and Utilization) method is trying to change the paradigm of CO2 from waste material into economic materials. The novelty of this research is the combined optimization of deterministic and stochastic methods with the Particle Swarm Optimization (PSO) algorithm to answer complex and non-linear problems in the CSSU (Carbon Sequestration Storage and Utilization) method. The CSSU method is an integration of geological, geophysical, reservoir engineering and engineering economics with the determination of technical and economic optimization of the use of CO2 produced as working fluid in a power generation system that has been conditioned through an injection-production system in geological formations. The CSSU research area is located in a sedimentary basin that has a giant gas field with 70% CO2 content. The Volumetric Storage Capacity for CO2 injection process in research area is 1,749.14 BCF or 94.01 MMTon which being calculated based on static modeling considering geological, geophysical and petrophysical aspects. A combination of Compositional, Geomechanics and Thermal reservoir simulation model had been conducted to determines the Storage Injection capacity and later to prove the CSSU method in which CO2 fluids will be utilized as working fluid, 1 case was built using 2 Injection Wells and 1 CO2 fluid Production Well. The simulation results show with 1 production well the total of CO2 fluid injected from 2 Injection wells can almost double the injection total capacity up to 1,150 BCF. The utilization of supercritical CO2 fluid as working fluid can produce 55 – 133.5 MMBTU/Day or 0.67 - 1.63 MW from 1 production well for 25 years timeframe. The CSSU method is optimized by deterministic and stochastic methods using the Particle Swarm Optimization (PSO) algorithm by looking the technical and economical aspects. The technical optimization aspect is being analyzed by electricity production versus well counts. The economical optimization is being analyzed by operational expenditure saving versus well counts and electricity produced versus NPV 10%. From both aspects the 4 injector wells case and NPV 200.00 MM US$ gives the most optimum result within technically and economically. The CSSU economic model proved with CSSU scheme the economical value is being increased by 57 MMUS$ after operating cost efficiency due to the electricity savings, 92 MMUS$ due to Carbon Trading which resulting the NPV 10% is 172.77 MMUS$.

Previous laboratory studies have shown that the combination of molasses and commercially-available fertilizer (NPK and ((NH4)2HPO4) can stimulate bacterial growth that decreases oil viscosity and interfacial tension (IFT). In this project, a huff-and-puff field implementation of MEOR nutrient injection has been conducted in two selected wells in Bentayan Field, South Sumatera, BN-38, and BN-81. The project objective is to evaluate and analyze the effect of nutrient injection towards oil recovery, changes of indigenous microbes that alter oil characteristics from the two selected wells during three months of post-nutrient injection monitoring phase. Changes of oil production were measured in-line after the gross produced liquid passes through a separator unit. The abundance of aerobic, anaerobic, and sulfate-reducing bacteria (SRB) was examined by using Total Plate Count (TPC) method, while anaerobic bacteria and SRB were cultivated using the Hungate technique. The chemical compositions of the oil were analyzed before and after nutrient injection by using gas chromatography-mass spectrometry (GC-MS) method. Fluid analysis of the oil (kinematic viscosity and interfacial tension) were measured by viscometer and IFT meter. The program of nutrient injection started with 500 bbls of first nutrient injection, followed by 100 bbls of post-flush 1, 500 bbls of second nutrient injection, 100 bbls of post flush 2, soaking for 14 days, 2000 bbls post-flush 3, and soaking for seven days. Based on the results of bacterial abundance analysis for three months, there was an increase of almost 1000-fold of total aerobic bacteria abundance and 10-fold of anaerobic bacteria abundance after nutrient injection. The expansion of bacterial abundance successfully changed the physical-chemical condition of the oil, decreasing oil viscosity by approximately 24% and IFT by 47%. In both wells, water cut values were decreased from 99% to 92%, and incremental oil was gained by 1395 barrels. Huff and puff nutrient injection seems to be low-cost EOR since it can be included in the daily operating cost with the additional cost of workforce, nutrient, monitoring, and analysis. Economic analysis calculation of this project resulted in Pay-out-Time (POT) by four months with an incremental gain of approximately 1750 barrels.

Despite the slow growth of EOR development in Indonesia, CO2 EOR has recently gained its momentum due to its versatility to diverse reservoir systems in Indonesia. Optimizing CO2 Injection in EOR activity is a must, since no incentive or tax holiday is given in Indonesia for this activity, contrary to the majority of policies around the globe. This study offers an innovative approach of CO2 flooding injection rate determination using closed loop optimization in project economic evaluation. Particle Swarm Optimization (PSO) algorithm was implemented as a method of optimizing the injection rate of CO2. These novel algorithms are known and proven to be able to work with massive number of datasets, as well as identifying and separating bad dataset(s). The result is optimum injection rate that brings maximum economic value to the project. Utilization of this method increases the NPV of the project by 10.4% and 12% increase in RF.

Biostimulation is a method of microbial enhanced oil recovery (MEOR) by injecting nutrients into an oil well to stimulate the growth of indigenous bacteria and their activities dealing with the increasing of oil recovery. This study aimed to obtain an optimum nutrient formulation at laboratory scale to be applied for MEOR field biostimulation pilot project. Field monitoring of chemical and bacterial composition in oil well was also carried out up to twelve weeks after biostimulation. The oil composition was analyzed by GC-MS and column chromatography. Bacterial community analysis was performed using 16S rRNA gene as the biomarker, which was sequenced by Illumina Miseq Sequencing. Based on Response Surface Methodology (RSM), the optimum nutrient concentrations that increase bacterial growth and decrease oil viscosity were molasses 5% (w/v), NPK 0.5% (w/v), and DAP 0.5% (w/v). The relative abundance of the bacteria and the community composition constantly changed during the monitoring process. Injection of molasses stimulated the growth of the hydrocarbonoclastic bacteria until the end of monitoring period.

This study is intended to evaluate, analyze and give recommendations for prudently utilizing gas production to maximize regional economic developments. A method to relate sectors and inter-regions, i.e., Inter-regional Input-Output (IRIO) Model, is employed to predict the impact of the upstream potential on the down stream. This techno-economic study examines the allocated gas from three gas field blocks altogether at Maluku and West Papua Provinces, i.e., Tangguh, Masela, and Kasuri, to supply the demands of (1) power plants; (2) fertilizer industries, and (3) petrochemicals industries. This study identifies development processes, investment parameters from pre-construction to operation, implementation parameters, and output parameters, both for the upstream and downstream sectors during 2016-2035. The IRIO model uses the 2010 IRIO Table published by BAPPENAS which consists of 35 sectors and 35 provinces. The shock data used to estimate the economic impacts include those data on infrastructure (CAPEX, OPEX, and supporting facilities), energy consumption (per kWh, per ton fertilizer, and per petrochemical product), workforce, and investments at a particular time. Six simulation modeling scenarios were developed to forecast the future performance of Gross Domestic Product (GDP)/Regional Gross Domestic Products (GRDP), Community Income, and Employment of 4 provinces in Eastern Indonesia. The results indicate that the regional economy could grow significantly by providing electricity first. Hence, the Petrochemical Industry gives more contribution than the Fertilizer Industry. The contribution (compared to BAU condition) increases sharply before taking a peak period and then declines in the year 2025. The best scenario results in a rise of the regional GDP at about 19 percent, wages at about 20 percent, and employment at about 55 percent. Maluku Province deserves the most economic improvement followed by West Papua Province. North Maluku and Papua provinces are not as well-off as those two provinces although there has been an indication of some employment in North Maluku Province. JEL Classification: E23Keywords: Gas resources, Input-Output, techno-economic, East Indonesia

Background:History matching is an indispensable phase in the workflow of reservoir analysis. Nevertheless, there is a considerable challenge in performing the procedure in a proper scientific manner due to the inherent nature of non-unique solutions from the many-unknown variables with limited known equations.Objective:In this study, we introduce the Ensemble Kalman Filter (EnKF) method complemented by the Region-Based Covariance Localization (RCL) scheme to address the aforementioned issue.Method:The algorithms work initially by modifying the covariance localization generated by Gaussian correlation model using region information such as facies or flow unit, in which the area within a region is spatially correlated. Subsequently, the correlation between distant areas in the region is eliminated, hence promoting better modification of the distribution of the parameters while maintaining the characteristics of the predefined geological model of the reservoir.Result:Result shows that RCL scheme is capable of enhancing the performance of EnKF procedure and produce parameter distributions that is close to the true model of the reservoir.Conclusion:Implementation of the proposed methodology ameliorates the accuracy and reliability of the history matching process, thus establishing better consideration in predicting reservoir performance.

Summary Indonesia oil production is in the stage of mature fields dominant having a current oil recovery factor of 32% leaving underground about 48.6 billion barrels of unrecoverable oil in place ( SKK Migas, 2016 ). At this stage, Enhanced Oil Recovery (EOR) is a mandatory choice to lift up the unrecoverable oil. SKK Migas and Bandung Institute of Technology (ITB) conducted a study in 2015 to evaluate the EOR potential of 136 fields in Indonesia. EOR screening processes was firstly conducted using the combination of Al-Adasani (2011) and Taber (1983 & 1997 ) studies to determine the most suited EOR methods for each reservoir. Following the screening results is forecasting additional reserves for each reservoir applying the US Department of Energy (DOE) formula. Based on the additional reserves, we are seeking the best field candidates for CO2 EOR. The CO2 EOR technology is a proven and mature technology. Through mapping the CO2 sources and the injected fields in Indonesia, several fields have been selected for pilot projects. The success of this CO2 EOR in Indonesia would mean the second life of national oil production. This paper shows one field project that is considered to be prospective.

Well placement optimization techniques that use reservoir simulations are currently taking advantage of using the Genetic Algorithm method and involve the output of a reservoir simulation, which is the hydrocarbon recovery, and thus, the technique requires running a reservoir simulation when finding a maximum value for the recovery. For a very large field of gas, a condensate reservoir would be very time consuming, and when there is only a limited amount of time for decision making, this approach would not be a sufficient technique. Of course, the conventional, traditional trial-and-error technique requires more effort. To address this very common challenge in field development planning, we propose the concept of transferring the manual traditional technique into a novel tool technique that employs Genetic Algorithm (GA), which can be used as a plug-in software application. This paper employs a specifically formulated Genetic Algorithm method for applications in well location optimization by introducing a newly proposed fitness function (objective function) that was constructed from basic reservoir engineering properties, i.e., permeability, porosity, oil saturation, pressure of reservoir, and thickness. Furthermore, this Genetic Algorithm method was then further extended to consider the drainage radius, existing wells, existence of faults and multiple layers, simultaneously. Hence, a software application has been developed that incorporates all of these concerns into a rapid tool. Reservoir modeling cases of oil and gas fields were used to test the proposed method, with the intention of showing the rapidness of finding the well locations, and as an additional output, this approach could yield a higher recovery than the previous technique, overall. The oil field is for cases in which multiple wells penetrate multi-reservoirs typically and penetrate selected reservoirs in test cases. However, the gas field application is for the case of horizontal well placements in which the direction and length are the optimized parameters, which are optimized by employing an Artificial Neural Network (ANN) method for the length optimization after having the best direction obtained from the Genetic Algorithm (GA) method. The proposed method can give hydrocarbon recovery results in a much faster way and even better values than the conventional method.

A pilot CCS project in Indonesia will be implemented in Gundih area, Central Java Province in Indonesia. Before the CO2 injection, the reservoirs for CO2 injection must be characterized carefully by conducting geophysical exploration as well as reservoir simulation, in order to make sure that the reservoir is suitable for CO2 storage. Here we report results of reservoir characterization and simulation for the determination of CO2 injection site in the Gundih area. Subsurface structures imaged on seismic reflection profiles indicate that the Ngrayong formation is one of the candidates for CO2 injection. We observed the outcrop of the Ngrayong formation and measured hydrological and geophysical properties (e.g., permeability, seismic velocity) of the rock samples obtained from outcrop and wells. The Ngrayong formation has layered structure and heterogeneous characteristics. Using (1) hydrological properties, (2) subsurface structures (i.e., geometry of the Ngrayong formation) and (3) physical properties predicted by integrating seismic and logging data via acoustic impedance inversion, we applied reservoir simulation and evaluated security of the CO2 injection sites.

During periods of low oil prices, profitability of field developments drops drastically. To help with this difficulty, we offer a cost-effective method of boosting hydrocarbon recovery by optimizing well locations. A new software program has been developed to optimize vertical well placements based on the Simulated Opportunity Index (SOI). SOI is an intelligent method to identify zones with high potential for production. The production potential of an oil & gas field area is empirically calculated from basic rock and fluid properties, and from reservoir pressure as its energy capacity. In order to obtain the best results, the original SOI formula (Molina et al., 2009) was extended to both oil and gas fields. With the new SOI software, it is possible to rank the best layers in each areal location, resulting in appropriate vertical well locations and perforated intervals in multilayer reservoirs. Furthermore, the developed software can account for the effects of existing wells and sealing faults to accommodate infill programs in brown fields and compartmentalized reservoirs. This paper describes how the software helps in obtaining optimal solutions with better accuracy than previous studies resulting in the highest recovery for three different case studies in an oil and gas field located in offshore Indonesia. The sensitivity analysis that was performed shows a strong relationship between SOI value and recovery factor. It validates that the higher SOI values selected by the software give higher recovery factors. The developed software could boost field development projects even in the period of low oil prices. On one hand, the proposed method could save money by minimizing the required number of wells. On the other hand, it could maximize profit by maximizing recovery.

East Natuna is well known for its humongous natural gas reserves with a high CO2 content. The high quantity of carbon dioxide requires implementation cutting-edge capture and storage process in its development plan which comes at a high cost. In order to increase the economic feasibility of the area, the impurities are proposed to be utilized CO2 as working fluid further to generate electricity through Enhanced Geothermal System (EGS). Carbon dioxide has been proven to be a better fluid for EGS as it could reach super critical state in much lower pressure and temperature compared to brine water. Sokang Trough Area in East Natuna Basin was selected as a candidate for pilot project due to its favorable geological condition.Carbon Capture Storage and Utilization (CCSU) especially EGS in sedimentary basin requires a suitable reservoir that fulfills several geological and engineering parameters. Firstly, it should porous enough to store fluid and permeable to flow it. The storage should also be deep enough to retain temperature above 87.98°F and pressure above 1071 psi in order to keep the CO2 in supercritical phase. Even further, EGS requires a minimum reservoir temperature of ±300°F to be technologically viable. In order to avoid vertical unintended migration, the reservoir should have high water saturation instead of gas saturation. Lastly, the seal should be able to confine the injected CO2 column within the storage.Formation evaluation workflow adapted for CCSU was employed in this study. Porosity, water saturation and permeability was estimated through deterministic approach. Formation pressure was calculated using Eaton’s equation. Reservoir temperature was estimated from available well testing data. Storage capacity was estimated for the whole structure with several cases. Considering all those parameters, several suitable reservoirs were able to be delineated in the CCS-1 well that is located within the East Natuna area.

Gas Flooding is one of the reservoir production management methods which commonly used in the petroleum industry in order to improve oil recoveries and maintaining reservoir pressure. This study discusses two methods in analyzing gas flooding, i.e., material balance analysis and reservoir simulation methods. The objective is to examine the ability and advantages of material balance method with respect to reservoir simulation. The studied field has three productive zones which consist of two light oil zones and one dry gas zone. Gas flooding scenario was conducted in one light oil reservoir and was modeled using both material balance and reservoir simulation methods to predict reservoir performance for about 14 years and the results were compared to investigate the effect of gas injection. The material balance method employs production, PVT, pressure data, and averaged properties of geological model for a single tank model. On the other hand, the reservoir simulation method uses a compositional model to investigate in detail the effect of injected gas to reservoir fluid behavior and the oil recovery The examinations indicate that the material balance analysis method shows the advantages of a requiring much less data, but could give reasonably accurate results. Furthermore, the application of material balance analysis in gas flooding could be used for gas injection rate optimization for a very short time manner and could assist for further development of reservoir simulation. For future development, material balance analysis could be used to analyze gas flooding scenario to save time and cost rather than reservoir simulation which takes longer time.

This paper contains reservoir simulation study of carbon storage at Gundih field in Central Java Island, Indonesia. Two different cases of injection simulation were performed and analyzed in this paper. The cases represent the conditions when the smallest and largest volumes of CO2areinjected into the subsurface to see the changes of reservoir that happen after the injection processes. The simulation result shows that when a larger amount of CO2 is injected into the targeted reservoir, it will migrate to the peak of anticline structure located in the southeast of CO2 injection well. The displacement of CO2 in the simulation progress shows that it will not reach the fault location. The geological model for synthetic seismogram calculation is then built based on the simulation reservoir result. The furthest displacement of CO2 is calculated on each case and described as the saturated CO2 layers. Forward modeling is performed to create synthetic seismic gather which will be processed to construct seismic section. The difference between the initial seismic section before the injection process and seismic section including saturated CO2 layer after the injection process will be evaluated by the potential of injected CO2 monitoring using time-lapse seismic survey in the Gundih field.

To maximize a horizontal well production, we need to determine the optimum direction and horizontal well length that maximizes the gas field recovery for a certain constant flow rate called by plateau rate. This problem is conventionally solved by using a reservoir simulation model and trial and error procedure that consumes considerably a lot of time and efforts. This study uses a random search method, i.e., Genetic Algorithm (GA), to solve this optimization problem and it very much eases to find the best well location with less time and efforts consumed.Along the general technique in directing a horizontal well towards the least principal stress of rocks, this study considers the geomechanics effects that influence the gas production performance. And also, the drainage area of horizontal well will be considered in this study to obtain the optimum horizontal well direction and length. In order to do this, a new proposed objective function for the GA has been constructed based on basic reservoir properties (i.e., porosity, permeability and gas saturation) and geomechanics properties (i.e., Young’s modulus and Poisson’s ratio). The results of the proposed method are validated using a reservoir model and economics evaluation.It may be concluded that the applying GA, with the appropriate objective function, can give accurate and faster results compared with the trial and error method using reservoir simulator, technically and economically, and also the proposed method is able to reduce the amount of works considerably time.

This research focuses on creating the prediction tools for the three key properties in coalbed methane (CBM) reservoir; the properties are gas content, Langmuir parameters, and permeability. Basically, their roles are to describe the gas in place and also future dynamic behavior of CBM reservoir. These three key properties are tried to be predicted with open-hole data as the inputs.It uses artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) to generate the prediction tools. It is started from data preparation and processing, then pattern or function identifications, and finalized by validation and testing. Several training algorithms are applied for ANN such as adaptive gradient descent (ANN_GDX), Levenberg-Marquardt (ANN_LM), resilient backpropagation (ANN_RP), scaled conjugate gradient (ANN_SCG), and Bayesian regularization algorithm (ANN_BR). The first fives employ the early stopping technique for regularization, and the last one does Bayesian regularization. On the other hand, the ANFIS will use only early stopping technique.Based on this research, it is concluded that both ANN and ANFIS are able to identify the patterns or function between open-hole log data and the three key properties (TKP). Furthermore, it can be concluded that ANN_LM, ANFIS, and ANN_BR are the best selected algorithms which resulted the lowest error of TKP’s predictions.

This study discusses an optimization model to obtain the optimal direction and length of horizontal wells in the oil field X. In the common practice in oil industries, the optimal direction and length are obtained from a trial and error method through a considerably time consuming reservoir simulation runs. Employing the basic reservoir properties data of the porosity, permeability, oil saturation, and location of each grid of the available reservoir model, Fuzzy Subtractive Clustering is used to classify grids. Furthermore, Fuzzy Logic optimization model is built to find the optimal direction and length of the horizontal well. Determination of the optimal direction and length is based on the oil recovery represented by a recovery factor that does not have any simple relationship with the basic reservoir properties data, but, through a reservoir simulation model that basically calculates fluid flows in porous media performances using a finite different formulation of the governing equation, i.e., the diffusivity equation. The filed case discussed in this study is one with the drilling starting point already known. The results of the study show that the methods of Fuzzy Subtractive Clustering and Fuzzy Logic are effective in determining the optimal direction and length simultaneously without running the reservoir simulation.

A gas field company concern is to seek the highest gas plateau rate as possible for a contract rate of sale gas to obtain the most economics field development scenario. On the other hand, the higher plateau rate causes more gas reserve left underground after end of the plateau rate for the rest of the life of a field, and this reserve left would be the government concern as the owner to maximize the field ultimate recovery. Having this fact, a new concept has been brought up to solve this optimization problem to find a meeting point between the two concerns by figuring the performance of the Recovery Factor (RF) at the end of the plateau rate or until the end of the contract; we name it Contractor Concern Recovery Factor (CCRF), and the performance of Remaining Recovery Factor (RFR) after the contract ends until the Field Ultimate Recovery Factor; we name it Government Concern Recovery factor (GCRF), with respect to Rate of Return (ROR), at which the two performances behave oppositely. We define this meeting point of CCRF and GCRF the optimum for both parties, a win-win solution for the gas field company as an operator and the government as the owner. Having this optimization concept, a reservoir model of a real gas field is run for a sensitivity analysis study and Design of Experiment (DoE) of important parameters for the optimum gas plateau flow rate, and the results suggest that Original Gas in Place, is the most influencing parameter followed by average permeability and gas relative permeability. Finally, a correlation is introduced by applying the Least-Square method to find a relationship between the optimum gas plateau flow rate with the parameters, and the resulted correlation has an acceptable accuracy.

Comparing the quality of basic reservoir rock properties is a common practice to locate new infills or development wells for optimizing an oil field development using a reservoir simulation. The conventional technique employs a manual trial and error process to find new well locations, which proves to be time-consuming, especially, for a large field. Concerning this practical matter, an alternative in the form of a robust technique was introduced in order that time and efforts could be reduced in finding best new well locations capable of producing the highest oil recovery. The objective of the research was to apply Genetic Algorithm (GA) in determining wells locations using reservoir simulation to avoid the manual conventional trial and error method. GA involved the basic rock properties, i.e., porosity, permeability, and oil saturation, of each grid block obtained from a reservoir simulation model, which was applied into a newly generated fitness function formulated through translating the common engineering practice in the reservoir simulation into a mathematical equation and then into a computer program. The maximum of the fitness value indicated a final searching of the best grid location for a new well location. In order to evaluate the performance of the generated GA program, two fields that had different production profile characteristics, namely the X and Y fields, were applied to validate the proposed method. The proposed GA method proved to be a robust and accurate method to find the best new well locations for field development. The key success of this proposed GA method is in the formulation of the objective function.

This paper is a laboratory study to investigate changes in rock and fluid properties in accordance with the most important vibroseismic parameters, i.e., frequency and amplitude. Experiment are conducted to obtain porosity, permeability, capillary pressure, end point of relative permeability, residual oil saturation which represent rock properties and oil viscosity whcih represent fluid properties. The results of the laboratory study on synthetic cores indicate that vibration stimulation can improve the porosity (ϕ) up to 15.3 % and the absolute permeability (kabs) up to 2.2 %, decrease the residual oil saturation (Sor) upto 53.6 %, decrease the irreducible water saturation (Swirr) up to 25 % (decrease the capillary pressure), increase the end point of oil relative permeability (kro) up to 73 %, and increase the end point of water relative permeability (krw) up to 76 %. This study also shows that there is an optimum frequency and amplitude that yield the most significant effect. The results show that frequency of 10 Hz and the maximum amplitude yield the maximum changes in these rock properties. Experiments on sandstone-field sample cores yield that the original porosity of 4.84 % - 26.73 % and absolute permeability of 231.46 mD – 1113.59 mD can be increased in percentage as a high as 53.9 % with an average of 36.6 % from the original porosity, and as high as 54.1 % with an average of 59.4 % from the original permeability. Moreover, the original residual oil saturation varying from 0.15 % - 0.57 % can be decreased up to 68 % with an average of 66.1 %. On carbonate cores, the results are not as encouraging as for sandstones’. The resulted optimum frequencies are 15 Hz and 10 Hz for reducing the residual oil saturation, and porosity/permeability, respectively. The differences between the residual oil saturation before and after the vibration vary from 14.18% up to 54.14% at this optimum frequency. The porosity changes vary from −18% up to 42 % (cek), and permeability changes vary from −28% up to 29 % (cek angkanya). This means that the vibration could not improve or even damage the rock properties if we do not apply the correct optimum frequency. In oil viscosity investigation, for the samples that has viscosity of 0,66 cp and 1.1 cp at 1000 psig, the viscosity measurement is conducted at temperature of 70oC, 85oC and 95oC for 1000 psig, 1500 psig, 2000 psig, 2500 psig and 3000 psig. For the lower viscosity sample at temperature of 70oC, the viscosity reduction at optimum frequency and maximum amplitude is proven to be increasing with the increasing pressure, it varies from 19,38% up to 22,6%. However, for temperature 85 oC, the viscosity reduction is about 30%, and for temperature of 95oC is about 15%. The highest viscoty sample yield viscosity reduction at temperature of 85oC ranging from 27–28%. The laboratory study results on the field core prove the success of the vibration stimulation technology with caution in selecting the optimum frequency, and hopefully, it may give contribution knowledge on the mechanisms of the vibroseismic technology.

This paper presents an analytical method to predict production performance of a well producing from a solution gas-drive reservoir. Many available analytical techniques published for a solution gas-drive reservoir have been intended mainly for data analysis techniques and they were proved to be valid only for one particular flow regime (i.e., transient flow). Many have used Inflow Performance Relationship (IPR) method to predict the well performance in these reservoirs. The shortcoming of this method is that it is based on regression to either field data or numerical simulation results. The data are sampled on one particular flow regime (i.e., boundary-dominated or pseudo-steady state flow). Hence, the outcomes are highly dependent on the condition at which the data are sampled. The analytical method in this paper addresses all flow regimes (transient, transition, and boundary dominated flow). The solution also considers that all phases (oil, gas, and water) can flow. This solution is also validated with results from numerical simulation for various fluids and reservoir properties.

This multidisciplinary paper presents microbial Huff and Puff project results with objectives: 1) to understand the mechanisms through relating laboratory work results, field implementations and a history matching process during reservoir simulation (2) to seek reliability of the microbial Huff &Puff based on improvements of the production performance parameters. After a thorough screening work was done, detail laboratory works produced fluid samples from selected wells was conducted, implementation programs were designed, and a set of equipment consisting of a cultivation tank, a mobile laboratory, and other supporting facilities was installed at the field. The implementation basically involves three stages: (1) preflush, cultivation solution, and post flush (2) injection of cultivation solution and post flush, and (3) injection post flush. Finally, this project conducted a 6-month monitoring program of microbe population and compositional analysis of the produced fluid samples, and production performance analysis of wells production data. The overall growth of bacteria had increased from 2×103 CFU / mL to 5×106 for 176 days in Well MJ122, on the other hand, Well MJ-125 can reach much higher up to 500×106 bacteria (CFU/ml) in 122 days. Using the GC/MS composition analysis for semi-quantitative analysis with standards internal reference marker ratio of Pr/Ph shows monthly biodegradation samples percentage before and after application up to about 34% at the 3rd month of monitoring. The well MJ-125 Structure Analysis of Bacteria Community resulted from Single Strand Conformation Polymorphism (SSCP) Method indicated that the number of Operational Taxonomy Unit (OTU) change during the production was relatively stable and this correspond to their Sorensen Index values. But, MJ-122 well indicated unstable community structure, however, the interaction between microbes which were injected with indigenous microbes was not competitive. The wells water cut versus gross rate profiles show that the MJ-122 water cut drop occurs at low production rate, whereas, the MJ-125 water cut drop occurs drastically both in low and high production rates. The well MJ-125 average oil rate gain is about 20%. However, both wells show peculiar production profiles by depicting a cycle of "a valley of low values". Finally, a history matching process supports understanding of the mechanisms.