Oil Production Rate Research Articles

Optimization of CO2 EOR and geological sequestration in high-water cut oil reservoirs

In terms of the collaborative optimization of CO2 flooding for Enhanced Oil Recovery (EOR) and CO2 sequestration, previous studies have co-optimized both cumulative oil production and CO2 sequestration by various algorithms. However, these solutions fail to optimize the CO2 injection schemes for high-water cut oil reservoirs. This paper presents an optimization methodology for CO2 flooding and sequestration in high-water cut oil reservoirs. The production optimization was carried out by adjusting the injection and production rate. To solve the proposed objective functions, the simultaneous perturbation stochastic approximation (SPSA) algorithm is applied in this paper, and the CMG-GEM module is utilized to simulate the reservoir production performance. A typical high-water cut reservoir in the Shengli oilfield was used to verify the feasibility of the presented methodology. In this paper, the production performance and net present value (NPV) for continuous gas injection under different water cuts were analyzed. The optimal timing of transforming from water flooding to gas displacement for the high-water cut reservoir was optimized. In addition, the optimal water–gas ratios for Water-Alternating-Gas (WAG) flooding were determined. The sensitivity of NPV to gas injection price and carbon subsidy was analyzed. The results show that when the gas price is 0.178 $/m3 and the carbon subsidy is 0.0169 $/m3, the optimal timing of transforming from water flooding to gas injection should be earlier than the time when the water cut is 0.82. Through the combination of NPV, cumulative oil production rate, and CO2 sequestration volume for WAG flooding, the optimal WAG ratio should be 1:2. The presented method in this paper considers various economic indicators and can optimize CO2 flooding and sequestration in high-water cut oil reservoirs efficiently, which can provide some guidance for the design of CO2 flooding schemes in high-water cut oil reservoirs.

Nodal Analysis of Naturally Flowing Wells in Faihaa Oil Field, Yamama Formation

This study focuses on the Yamama Formation, a significant carbonate reservoir in southern Iraq that is one of the most important productive reservoirs in the region. The Formation is characterized by porous limestone interspersed with thin layers of argillaceous and tight limestone. The Yamama reservoir in the Faihaa oil field is divided mainly into four units; YA, YB, YC, and YD. YA and YB units are considered to be the most important oil-bearing subunits due to their good petrophysical properties. The main objective of the study is to determine the optimum production rates of four naturally flowing wells in the Faihaa oil field using the Inflow Performance Relationship and Vertical Lifting Performance curves. The study investigates four critical parameters; tubing size, water cut, reservoir pressure, and wellhead pressure, and their impact on well performance. The study finds that wellhead pressure is the primary determinant of well performance, and deviations from the original tubing size have adverse effects on well performance. An increase in water cut beyond the recommended threshold, coupled with a reduction in reservoir pressure, results in decreasing well performance. The study underscores the importance of careful monitoring and analysis of these parameters to sustain and enhance well performance in the Faihaa oil field, providing valuable insights for well operators and petroleum engineers. The study's findings can be used to optimize well performance and increase oil production rates, with significant implications for the petroleum industry.

Gas lift allocation optimization using Pipesim network optimizer

This work maximizes oil production from a network of gas lift wells and determines the optimal amount of lift gas with PIPESIM network optimizer. PIPESIM was used to build a couple wellbore-flowline model and its network optimizer was utilized to maximize oil production from a network of gas lifted wells. Two wells were created (Well A and B) and connected to their respective flow lines to a manifold and from the manifold to the central processing facility (CPF) represented by a sink node. A comparison of the oil production rate from Well B with and without constraint on water production rate shows that 7337.432STB/day of oil was produced without any constraints on water production while 7373.479 STB/day of oil was produced with constraint on water production and Well A, 280.6693 STB/day of oil was produced without any constraints on water production while 3.266503 STB/day of oil was produced with constraint on water production. For the injection of 4MMscf/day of gas with a constraint of 1800STB/day on water production, results reveals that a total gas lift rate of 3.723612MMscf/day was required to lift a total oil rate of 7484.669STB/day. Therefore, out of the 4MMscf/day of gas available, 3.723612MMscf/day should be allocated to the network to optimized production.

Application of Flue Gas Foam-Assisted Steam Flooding in Complex and Difficult-to-Produce Heavy Oil Reservoirs.

In order to study the mechanism of improving recovery efficiency of complex difficult-to-recover heavy oil reservoirs and explain the interaction and migration law of flue gas, foam, and steam, this paper designed the experiment of flue gas influence on heavy oil flow and the experiment of flue gas foam displacement in complex difficult-to-recover heavy oil model. The results show that the flue gas has expansion and an energy enhancement effect. Moreover, the interfacial tension of heavy oil can be reduced, and the higher the CO2 content in flue gas, the more beneficial it is to reduce the interfacial tension. When there is an interlayer in the reservoir, the gas can form a "puncture" in the interlayer, which provides a channel for the subsequent upward expansion of steam, so that the upper part of the interlayer can be used to expand the steam sweep range. The main mechanism of improving heavy oil recovery with flue gas foam is that the foam regulates fluid mobility and improves the thermal sweep efficiency of steam. In addition, the injected foam can emulsify with heavy oil, reduce the viscosity of heavy oil, and improve the fluidity of heavy oil. Finally, the maximum oil production rate increased from 1.809 to 2.455 g/min, and the recovery rate increased from 44.3 to 68.8%.

Simulation and performance prediction of partially naturally fractured reservoirs under solution gas drive primary recovery and gas injection processes

The study presented in this paper has multiple objectives. First, a simulation model for partially naturally fractured reservoirs under solution gas drive is developed. The model considers the major key parameters controlling fluid flow in the reservoir, including fracture intensity and distribution, instantaneous gas/oil segregation due to vertical capillary continuity, gas/oil gravity drainage, and reinfiltration of the drained oil to the lower matrix. Once the model is well-established, it is used to study the reservoir performance under two recovery processes: primary depletion and gas injection. A detailed investigation of the sensitivity of the ultimate oil recovery to the fracture intensity, oil production rates, and gas injection rates is performed. The findings of this study indicate that the ultimate oil recovery of low-fracture intensity reservoirs subjected to the depletion drive process is insensitive to production rates. However, for moderate- to high-fracture intensity reservoirs and low production rates, the recovery increases with increasing fracture intensity. Conversely, for moderate- to high-fracture intensity reservoirs and high production rates, the recovery is not significantly affected. For the gas injection mechanism, it is found that the ultimate oil recovery is a function of both the fracture intensity and gas injection rate. Furthermore, three fracture intensity ranges are identified: low, medium, and high. For the low- and high-fracture intensity ranges, the recovery increases with increasing gas injection rates and fracture intensity. However, for the medium fracture intensity ranges, the recovery behaves differently. It increases at low gas injection rates and decreases at high injection rates as the fracture intensity increases. New equations relating the cumulative oil production to the production rates, gas injection rates, and fracture intensity are also presented.

A new progressive and efficient production sharing Contract for upstream oil and gas industry

After the pandemic, the structure of the upstream petroleum business changed. Exploration and production (E&P) companies no longer base their investment decisions on the geological prospectivity of the offered blocks but rather on how the fiscal systems adopted by host governments behave under such uncontrolled and unexpected events. Many E&P companies couldn't survive during the pandemic. This is because many governments adopt regressive and front-end-loaded systems that are not designed to accommodate such periods of low oil prices. This research, therefore, presents a new methodology to design the main fiscal elements of production sharing contracts (PSCs). These elements are: royalty, cost recovery limit, production sharing, and income tax. The performance of the proposed PSC model under different oil prices, production rates, and development costs is investigated using deterministic sensitivity analysis. Our results showed that the proposed PSC model is a progressive and win-win fiscal system. The performance of the proposed PSC model is compared with Ghana's fiscal system and the new Nigeria's Petroleum Industry Act (PIA 2021) under a wide range of oil prices, production rates, development, and operating costs. The comparative study proved that the proposed PSC model enhances the survival and sustainability of E&P companies during periods of falling oil prices. The comparative study proved that, unlike the proposed PSC model, Ghana's fiscal system and PIA 2021 are regressive. Furthermore, the contracting parties are not in a win-win situation.

The Economic Evaluation of Various Production Scenarios for Zubair Reservoir in the Kifl Field

This work evaluates the economic feasibility of various production scenarios for the Zubair reservoir in the Kifl oil field using cash flow and net present value (NPV) calculations. The Kifl field is an exploratory field that has not yet been developed or assessed economically. The first well was drilled in 1960, and three other wells were later drilled to assess the oil accumulation, so in this research, Different production scenarios were evaluated economically. These scenarios were proposed based on the reservoir model of the Zubair formation in the field. The research methodology used QUE$TOR software to estimate capital expenditures (CapEx) and operating expenditures (OpEx) based on field-level data, production profiles for each scenario, and produced fluid properties. CapEx estimates were mainly based on the number of drilled wells and plateau duration, while OpEx estimates were based on the number of barrels produced from each scenario. Cash flow values were estimated each year in the production interval after subtracting water handling costs, CapEx, and OpEx from the gross revenue of oil produced. The NPV for each scenario was then calculated using a 5% interest rate, and the most economical option was determined by comparing the NPV values of each scenario. Case-3 scenario was identified as the most economical option, with a high NPV value of approximately 612 MM$, an oil production rate of 10 MSTB/d, a Plateau duration of 3 years, a total number of wells of 11, and an Initial water cut of 13 %.

Recalculation of Pump Speed and Stroke Length to Increase Production Rate and Volumetric Efficiency of HPU Well BTN-01

In the "BTN-01" well, one of the wells is owned by KSO PT PERTAMINA EP PT Sarana GSS Trembul. The "BTN-01" well is installed with a hydraulic pumping unit pump that experiences decreasing production, and based on tests on January 30, 2023, the output of the "BTN-01" well produces only 216 bfpd with 98% of water cut. The pump operates on 8 SPM of pump speed and 74-inch stroke length, with 43% of volumetric efficiency. As per theory, a proper design of hydraulic pumping unit pump will produce a volumetric efficiency value above 70%. To overcome this problem, it is necessary to perform research, started with an evaluation on the existing design and continue to quantitative research by recalculating the optimum production rate from IPR calculation, recalculating pump speed, recalculating pump stroke length, and recalculating volumetric efficiency to achieve higher value on both production rate and volumetric efficiency of the pump. In the "BTN-01" well, IPR was calculated by the Wiggins method, and it is known that the oil production rate is 5.53 bopd, 421.96 bwpd of water rate, and the fluid production rate is 427.5 bfpd. Based on the results of HPU redesign and recalculation of the optimum production rate, the new value of pump speed is 7 SPM (existing 8 SPM), the pump stroke length is 70 inches (existing 74 inches) with a volumetric efficiency of 78.45% (existing 43%) that can be called optimal.

Optimization of high-temperature gas parameters and analysis of the impact of oil shale thickness on the in situ development process

In situ shale oil extraction is an efficient and environmentally friendly development method. This article employs in situ extraction technology to heat shale oil, optimizing its high-temperature gas parameters based on indicators such as cumulative oil production rate and cumulative power consumption in the shale oil region. Using the optimized high-temperature gas parameters, an exploration of shale oil with different thicknesses was conducted. The results indicate that out of nine different high-temperature gas parameters, five cannot induce complete fracturing in the shale oil region. Models II, III, I, and IV each reach the onset of shale oil fracturing in the entire region after 870, 900, 1020, and 1080 days, respectively. The heating rate factor peaks at 1620, 1620, 1800, and 1980 days, with values of 0.277, 0.305, 0.317, and 0.32, respectively. Compared to Model II, the peak of the heating rate factor in Model I is the highest, showing an increase of 15.52%. Among these models, Model I has the lowest unit volume power consumption at 1063.41 kW/m3. The unit volume power consumption in Model I exhibits the most significant reduction, with a maximum decrease of 16.45% compared to the other three shale oil models of different thicknesses. The research results provide a basis for the engineering development of shale oil.

Investigation of the potential of application Frac-Pack technology as a solution to the problem of sand control and increasing the production of high-viscosity oils at M field

Background: In this paper, the potential of applying hydraulic fracturing with the installation of a gravel pack in the Cretaceous horizons of the M field is investigated. The relevance of the work is due to the fact that today the selection of optimal technologies to increase the rate of oil production from the Cretaceous deposits an open platform for proposals and discussions. The low oil recovery coefficient is due to the existing restrictions on the oil production process: high oil viscosity, the presence of gas and water-oil zones, abundant sand production in a poorly cemented reservoir.
 Aim: The purpose of the work is to study the potential application of Frac-Pack technology in the wells of the M field.
 Materials and methods: In order to assess the potential of using Frac-Pack in a special FracPro software, a model of geomechanical and filtration properties of the formation was developed and a simulation of the design with various injection parameters was performed. Based on the results of laboratory studies of the content of mechanical impurities in liquids sampled at the M field, the diameter of the proppant and the flow section of the filter were determined.
 Results: As a result of the study, hydraulic fracturing was simulated for a well from a sample of potential candidates, the oil flow rate was calculated after the application of the proposed technology, materials for the implementation of Frac-Pack technology were selected based on laboratory studies.
 Conclusion: The study showed that the proposed pre-development strategy is the most optimal for shallow wells of the M field, as it is aimed not only at solving the problem of combating the removal of mechanical impurities, but also increasing oil production.

Oil Production Rate Forecasting by SA-LSTM Model in Tight Reservoirs

Abstract The accurate forecasting of oil field production rate is a crucial indicator for each oil field’s successful development, but due to the complicated reservoir conditions and unknown underground environment, the high accuracy of production rate forecasting is a popular challenge. To find a low time consumption and high accuracy method for forecasting production rate, the current paper proposes a hybrid model, Simulated Annealing Long Short-Term Memory network (SA-LSTM), based on the daily oil production rate of tight reservoirs with the in situ data of injection and production rates in fractures. Furthermore, forecasting results are compared with the numerical simulation model output. The LSTM can effectively learn time-sequence problems, while SA can optimize the hyperparameters (learning rate, batch size, and decay rate) in LSTM to achieve higher accuracy. By conducting the optimized hyperparameters into the LSTM model, the daily oil production rate can be forecasted well. After training and predicting on existing production data, three different methods were used to forecast daily oil production for the next 300 days. The results were then validated using numerical simulations to compare the forecasting of LSTM and SA-LSTM. The results show that SA-LSTM can more efficiently and accurately predict daily oil production. The fitting accuracies of the three methods are as follows: numerical reservoir simulation (96.2%), LSTM (98.1%), and SA-LSTM (98.7%). The effectiveness of SA-LSTM in production rate is particularly outstanding. Using the same SA-LSTM model, we input the daily oil production data of twenty oil wells in the same block and make production prediction, and the effect is remarkable.

Comparative Assessment of Conventionally and Locally Sourced Surfactants for Enhancing Steam Flooding Techniques for Heavy Oil Recovery in Niger Delta

The recovery of heavy oil reserves presents a significant challenge in the petroleum industry due to its high viscosity and poor mobility characteristics. Steam flooding, as a thermal Enhanced Oil Recovery (EOR) technique, has shown promise in mobilizing heavy oil deposits. However, the limited success of conventional steam flooding in heavy oil reservoirs necessitates innovative approaches. This study explores the utilization of conventional and locally sourced surfactants in surfactant-enhanced steam flooding (SESF) for heavy oil recovery in the Niger Delta region. The study examines the selection, formulation, and injection of surfactants specifically tailored for heavy oil reservoir conditions. Laboratory experiments, core flooding tests, and numerical simulations are conducted to evaluate the impact of surfactants on interfacial tension reduction, wettability alteration, and improved oil mobility in heavy oil reservoirs subjected to steam flooding. The project’s findings demonstrate the significant potential of (SESF) for heavy oil recovery. The synergistic effects of surfactants and steam, including the reduction of oil-water interfacial tension, lead to increased oil production rates, reduced steam consumption, and enhanced sweep efficiency. Furthermore, economic assessments are conducted to evaluate the feasibility of implementing this approach on a field scale, considering both technical and economic aspects. This study not only contributes valuable insights to the field of heavy oil recovery but also underscores the practicality of surfactant-enhanced steam flooding as an environmentally responsible solution for unlocking the vast heavy oil reserves worldwide. The research bridges the gap between laboratory findings and real-world applications, offering a promising path forward for the sustainable development of heavy oil resources.

Oil Production Optimization Using Q-Learning Approach

This paper presents an approach for optimizing the oil recovery factor by determining initial oil production rates. The proposed method utilizes the Q-learning method and the reservoir simulator (Eclipse 100) to achieve the desired objective. The system identifies the most efficient initial oil production rates by conducting a sufficient number of iterations for various initial oil production rates. To validate the effectiveness of the proposed approach, a case study is conducted using a numerical reservoir model (SPE9) with simplified configurations of two producer wells and one injection well. The simulation results highlight the capabilities of the Q-learning method in assisting reservoir engineers by enhancing the recommended initial rates.

Prospects for the arctic shelf development

Nowadays development of the Arctic shelf and the continental slope resources is one of the most promising areas in the world oil and gas industry growth. Russia, unlike other countries with Arctic hydrocarbon reserves, implements only a few projects on the Northern shelf due to the difficult natural and climatic conditions and ecological vulnerability of the territory. The purpose of the article is to substantiate the feasibility of hydrocarbon resources development on the Russian Federation Arctic shelf in the long term. The article analyzes the external and internal factors affecting the decline in the rate of oil and gas exploration and production in this region in recent years. It considers state support measures reflected in the Strategy for the Russian Arctic Zone Development and Ensuring National Security – 2035, and a number of other laws and regulations developed and adopted after 2014. Special attention is given to the development of high-tech imports phase-out; specialists training; science, education and business integration; reducing the tax burden; environmental issues. The tasks facing state authorities, oil and gas companies, scientific and educational organizations have been identified that will lead to the successful Arctic shelf reserves development.

ФОРМИРОВАНИЕ И РЕДУКЦИЯ БАЗЫ ЗНАНИЙ ДЛЯ ОЦЕНКИ ЦЕЛЕСООБРАЗНОСТИ БУРЕНИЯ СКВАЖИН НА НЕФТЯНЫХ МЕСТОРОЖДЕНИЯХ

The article discusses the issues of forming and reducing a fuzzy knowledge base designed to assess the feasibility of drilling wells in oil fields as part of a decision support system. The initial knowledge base was formed using a specially developed software package that implements neuro-fuzzy modeling technology based on a group of fuzzy neural networks. To train them and form a knowledge base, initial data were required that described the dependence of the influence of the characteristics of producing wells on their efficiency in terms of the production rate of oil produced. The initial data for the analysis of 3187 records were presented for 46 oil field objects with carbonate reservoirs. Each record was characterized by the values of the input parameters “absolute rock permeability”, “rock porosity”, “average flow rate of neighboring wells” and “minimum water cut of neighboring wells”. The output parameter determined the efficiency of the well as the average oil flow rate for the first 3 years of operation. When training a team of 5 fuzzy neural networks, 5 gradations of input parameters were selected. The total number of fuzzy rules in the original knowledge base was 319. To assess its classification ability, the values of the Accuracy, Precision, Recall and F1-Score metrics were calculated. To reduce the knowledge base, a specially developed software package was used that implements 2 stages of genetic optimization: structural-parametric transformations of the fuzzy rules of the original knowledge base to obtain an intermediate knowledge base and minimizing the composition of the fuzzy rules of the intermediate knowledge base to obtain the desired (reduced) knowledge base. As a result of the reduction, the quantitative composition of knowledge bases, as well as the values of classification quality metrics, changed. The number of rules in the intermediate knowledge base was 131, and in the reduced one - 33. The overall reduction in the volume of the knowledge base was 89.66%. After the 1st stage of reduction, the values of the Accuracy, Precision, Recall and F1-Score metrics on the test data set increased, respectively, by 7.88%, 6.8%, 0.34% and 0.53%. After the 2nd stage of reduction, the overall increase in the values of these metrics was, respectively, 9.02%, 7.55%, 1.58% and 1.68%. The results obtained were tested at Tatneft and used to form and optimize the knowledge base when developing a decision support system for assessing the feasibility of drilling wells in oil fields.

Application of Artificial Intelligence in Predicting Oil Production Based on Water Injection Rate

The utilization of artificial intelligence (AI) has become imperative across various domains, including the oil and gas industry, which covers several fields, including reservoirs, drilling, and production. In oil and gas production, conventional methods, such as reservoir simulation, are used to predict the oil production rate. This simulation requires comprehensive data, so each process step takes a long time and is expensive. AI is urgently needed and can be a solution in this case. This research aims to apply AI techniques to forecast oil production rates based on water injection rates from two injection wells. Three wells are connected with a direct line drive pattern. Three different AI methods were applied, including multiple linear polynomial regression (PR), multiple linear regression (MLR), and artificial neural networks (ANN) in constructing oil production rate prediction models. Actual field data of 1180 data are used, including water injection rate data from two injection wells and oil production history data from one production well. The dataset has been split randomly into 80% training and 20% allocated for testing subsets. The training data is used to build predictive models, while the testing data is used to validate model performance. Comparative analysis selects the model with the lowest root mean square error (RMSE) and the highest R^2 test value. Results demonstrate that the ANN model achieves the smallest Root Mean Square Error (RMSE) of 0.142 and the highest R^2 test value of 16.2%, outperforming the PR and MLR methods. The ANN prediction model provides a rapid and efficient approach to estimating oil production rates.

Application of Artificial Intelligence in Predicting Oil Production Based on Water Injection Rate

The utilization of artificial intelligence (AI) has become imperative across various domains, including the oil and gas industry, which covers several fields, including reservoirs, drilling, and production. In oil and gas production, conventional methods, such as reservoir simulation, are used to predict the oil production rate. This simulation requires comprehensive data, so each process step takes a long time and is expensive. AI is urgently needed and can be a solution in this case. This research aims to apply AI techniques to forecast oil production rates based on water injection rates from two injection wells. Three wells are connected with a direct line drive pattern. Three different AI methods were applied, including multiple linear polynomial regression (PR), multiple linear regression (MLR), and artificial neural networks (ANN) in constructing oil production rate prediction models. Actual field data of 1180 data are used, including water injection rate data from two injection wells and oil production history data from one production well. The dataset has been split randomly into 80% training and 20% allocated for testing subsets. The training data is used to build predictive models, while the testing data is used to validate model performance. Comparative analysis selects the model with the lowest root mean square error (RMSE) and the highest R^2 test value. Results demonstrate that the ANN model achieves the smallest Root Mean Square Error (RMSE) of 0.142 and the highest R^2 test value of 16.2%, outperforming the PR and MLR methods. The ANN prediction model provides a rapid and efficient approach to estimating oil production rates.

Development of an Intelligent Oil Field Management System based on Digital Twin and Machine Learning

This article introduces an innovative approach to oil field management using digital twin technology and machine learning. A detailed experimental setup was designed using oil displacement techniques, equipped with sensors, actuators, flow meters, and solenoid valves. The experiments focused on displacing oil using water, polymer, and oil, from which valuable data was gathered. This data was pivotal in crafting a digital twin model of the oil field. Utilizing the digital twin, ML algorithms were trained to predict oil production rates, detect potential equipment malfunctions, and prevent operational issues. Our findings highlight a notable 10-15% improvement in oil production efficiency, underscoring the transformative potential of merging DT and ML in the petroleum industry.

Optimization of Horizontal Well Location and Completion to Improve Oil Recovery for an Iraqi Field

Exploitation of mature oil fields around the world has forced researchers to develop new ways to optimize reservoir performance from such reservoirs. To achieve that, drilling horizontal wells is an effective method. The effectiveness of this kind of wells is to increase oil withdrawal. The objective of this study is to optimize the location, design, and completion of a new horizontal well as an oil producer to improve oil recovery in a real field located in Iraq. “A” is an oil and gas condensate field located in the Northeast of Iraq. From field production history, it is realized the difficulty to control gas and water production in this kind of complex carbonate reservoir with vertical producer wells. In this study, a horizontal well design with multi-stage completion is studied and proposed to find optimal oil recovery in the southeast region of the selected field. A bulk oil well sector model is used to simulate the fluid flow of a single-porosity/single-permeability model. Then, a sensitivity analysis has been run to optimize; the well trajectory path, different scenarios on well oil and water production potential, and well completion design. The result of the well sector simulation indicates that the well trajectory with an Azimuth of 89 degrees and with a multi-stage completion design has better production performance under water production constraints. Optimum oil production rates of 1000 to 2000 STB/day, as delaying and controlling early gas and water production challenges is achieved.

Carbon Stock Estimation on Oil Palm Plantations and Oil Palm-Based Agroforestry in Gunung Mas Regency

Central Kalimantan has one of the highest rates of deforestation and palm oil production in Indonesia. These changes have ecological impacts such as loss of animals, loss of water absorption functions, and increased carbon emissions. Agroforestry is a synergistic planting system between agricultural crops and forest stands to maintain the ecological balance. Sengons are legume plants that can be utilized in agroforestry systems. This study aimed to calculate the amount of belowground and aboveground carbon stocks on palm oil plantations and agroforestry lands consisting of palm oil and sengon trees in Manuhing and Rungan Barat districts. Belowground carbon consist of soil carbon, which is affected by the soil depth, bulk density, and soil C-organic value. Aboveground carbon consists of the sum of litter carbon, undergrowth carbon, and top stand vegetation carbon. Belowground carbon was measured using both disturbed and undisturbed methods. Litter and undergrowth carbon were measured using a destructive method, then top stand vegetation was measured by the allometric equations using breast height diameter. The comparison from all carbon pool shows that the palm oil plantations (2106,520 tons/ha) was higher than agroforestry lands (1834,734 tons/ha). This difference is strongly influenced by the potential of the different in belowground carbon stock for each land-use type. The highest potential carbon stock from this study was owned by belowground carbon stock. In the Manuhing district, belowground carbon stock was led by agroforestry lands (1786,907 tons/ha), whereas in the Rungan Barat district was led by palm oil plantations (1756,291 tons/ha).