Reservoir Characterization for site Selection in the Gundih CCS Project, Indonesia

Tsuji, Takeshi; Matsuoka, Toshifumi; Kadir, Wawan Gunawan A.; Hato, Masami; Takahashi, Toru; Sule, Mohammad Rachmat; Kitamura, Keigo; Yamada, Yasuhiro; Onishi, Kyosuke; Widarto, Djedi S.; Sebayang, Rio I.; Prasetyo, Agung; Priyono, Awali; Ariadji, Tutuka; Sapiie, Benyamin; Widianto, Eko Didik; Asikin, Ariesty Ratna

doi:10.1016/j.egypro.2014.11.666

Cited by 23 publications

(17 citation statements)

References 17 publications

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“…Well logs are useful not only to identify bedding planes, fractures, reservoirs, and fault zones, but also to infer information, such as the physical properties of rocks, lithology characterization, and stress fields. Well logs are of great value to carbon capture and storage (Förster et al, 2010; Tsuji et al, 2014), geothermal energy utilization (Vallier et al, 2019; Vik et al, 2018), earthquake study (Kim et al, 2018; H. Li et al, 2013), groundwater science and engineering, and the exploitation of subsurface resources, such as oil, gas, and minerals (Alexeyev et al, 2017; Wendt et al, 1986). However, obtaining well logs is usually expensive and time consuming, which leads to two challenges: (1) The full set of logs is not always available in practice; and (2) even when the logs are measured, the number of logs is often small, resulting in a small‐data problem (few‐shot learning).…”

Section: Introductionmentioning

confidence: 99%

Well Log Generation via Ensemble Long Short‐Term Memory (EnLSTM) Network

Chen

Zhang

2020

Geophysical Research Letters

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In this study, we propose an ensemble long short-term memory (EnLSTM) network, which can be trained on a small data set and process sequential data. The EnLSTM is built by combining the ensemble neural network and the cascaded LSTM network to leverage their complementary strengths. Two perturbation methods are applied to resolve the issues of overconvergence and disturbance compensation. The EnLSTM is compared with commonly used models on a published data set and proven to be the state-of-the-art model in generating well logs. In the case study, 12 well logs that cannot be measured while drilling are generated based on the logs available in the drilling process. The EnLSTM is capable of reducing cost and saving time in practice. Plain Language Summary A novel neural network, called EnLSTM, is proposed by combining the ensemble neural network, which has good performance on small-data problems, and the cascaded long short-term memory network, which is effective at processing sequential data. The EnLSTM's capability of processing sequential data based on a small data set is especially suitable for generating synthetic well logs. In addition, two perturbation methods are used to ensure that the EnLSTM can be fully trained in practice. In the experiments, the EnLSTM achieved the current best results on a published well log data set, and its application value is verified in a case study.

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Section: Introductionmentioning

confidence: 99%

Well Log Generation via Ensemble Long Short‐Term Memory (EnLSTM) Network

Chen

Zhang

2020

Geophysical Research Letters

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“…The reason is when the fault is located quite close to the injection wells, the water injection can create more CO 2 leaking at the fault due to the pressure caused by water injection (Leits, 2016). In the targeted formation, the distance between the targeted anticline structure and the fault is quite near, which is around 400 m (Tsuji et al, 2014).…”

Section: Resultsmentioning

confidence: 99%

Forward Modeling Time-Lapse Seismic based on Reservoir Simulation Result on The CCS Project at Gundih Field, Indonesia

Asikin¹,

Priyono²,

Ariadji³

et al. 2017

MAS

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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 CO 2 are injected 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 CO 2 is injected into the targeted reservoir, it will migrate to the peak of anticline structure located in the southeast of CO 2 injection well. The displacement of CO 2 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 CO 2 is calculated on each case and described as the saturated CO 2 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 CO 2 layer after the injection process will be evaluated by the potential of injected CO 2 monitoring using time-lapse seismic survey in the Gundih field.

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“…Indonesia, as a major carbon emitter, has been developing its CCS program in the past 11 years 17–22 . The first project which is still in progress is the Gundih CCS pilot project 23–27 . Studies suggest that depleted oil/gas field and saline aquifers are potential storage sites in the country but face challenges due to their remoteness from CO 2 sources 18,22 .…”

Section: Introductionmentioning

confidence: 99%

“…[17][18][19][20][21][22] The first project which is still in progress is the Gundih CCS pilot project. [23][24][25][26][27] Studies suggest that depleted oil/gas field and saline aquifers are potential storage sites in the country but face challenges due to their remoteness from CO 2 sources. 18,22 In addition to these sites, the abundance of geothermal fields with intermediate volcanic reservoir lithology 28 as potential CO 2 storage sites is yet to be investigated.…”

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confidence: 99%

Preliminary geochemical investigation of a possible CO₂ injection in the Ungaran geothermal field, Indonesia: equilibrium and kinetic modeling

Utomo

Güleç

2020

Greenhouse Gases

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Carbon capture and storage (CCS) is considered to be an effective method to mitigate anthropogenic carbon emissions that have been the major cause of global warming. One of the possible sites to store CO 2 is in geothermal reservoirs. In this study, an attempt to simulate CO 2-brine reservoir rock interaction inside a geothermal reservoir is carried out using the PHREEQC program. The study utilizes published rock mineralogy of the assumed reservoir lithology and chemistry of the hot water in the Ungaran geothermal field, Java, Indonesia. The simulation is based on equilibrium and kinetic modeling and assumes a single stage CO 2 injection kept at a constant temperature and pressure. The amount of injected CO 2 is determined by solubility modeling of CO 2 in hot water under estimated reservoir conditions. The modeling predicted (i) the effect of solubility trapping at early stages of CO 2-brine rock interaction, (ii) dissolution of Ca-bearing silicates (plagioclases) coupled with calcite precipitation as a potential chemical processes relevant to a possible CO 2 mineralization, (iii) progressive transition from solubility to mineral trapping becoming significant after 30 days following injection, (iv) minor porosity increase (∼0.5%), and (v) achievement of equilibrium between CO 2-brine-rock in 10 years after injection. Sensitivity analysis associated with the uncertainties for altering mineral proportion and rock porosity reveal no significant change in the ability of the modeled reservoir to trap injected CO 2 into mineral phases. Concerning the CCS studies so far carried out in geothermal fields in volcanic reservoirs, this modeling comprises one of the first performed for fields with intermediate volcanics. The result from this study can be utilized as foreknowledge for possible future CCS operations in Indonesian geothermal fields.

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Reservoir Characterization for site Selection in the Gundih CCS Project, Indonesia

Cited by 23 publications

References 17 publications

Well Log Generation via Ensemble Long Short‐Term Memory (EnLSTM) Network

Well Log Generation via Ensemble Long Short‐Term Memory (EnLSTM) Network

Forward Modeling Time-Lapse Seismic based on Reservoir Simulation Result on The CCS Project at Gundih Field, Indonesia

Preliminary geochemical investigation of a possible CO₂ injection in the Ungaran geothermal field, Indonesia: equilibrium and kinetic modeling

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Reservoir Characterization for site Selection in the Gundih CCS Project, Indonesia

Cited by 23 publications

References 17 publications

Well Log Generation via Ensemble Long Short‐Term Memory (EnLSTM) Network

Well Log Generation via Ensemble Long Short‐Term Memory (EnLSTM) Network

Forward Modeling Time-Lapse Seismic based on Reservoir Simulation Result on The CCS Project at Gundih Field, Indonesia

Preliminary geochemical investigation of a possible CO2 injection in the Ungaran geothermal field, Indonesia: equilibrium and kinetic modeling

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Preliminary geochemical investigation of a possible CO₂ injection in the Ungaran geothermal field, Indonesia: equilibrium and kinetic modeling