Optimization of Relief Well Design Using Artificial Neural Network during Geological CO2 Storage in Pohang Basin, South Korea (original) (raw)

Neural Network Based Performance Evaluation of a Waterflooded Oil Reservoir

In this paper, we considered the use of neural networks in the identification and prediction of a waterflooded reservoir consisting of eight injection wells and one production well with a 40% porosity. The data used for the non-linear identification was generated from a reservoir modelled in MATLAB Reservoir Simulation Toolbox (MRST). Likewise, in this study, the effect of number of hidden neurons on the accuracy, Mean Squared Error and oil production prediction of the reservoir was investigated. The study asserted the efficacy of the neural networks as regards to its predictive capacity. For the oil production rate, a mean squared error was recorded to be minimal for 2 hidden neurons as compared to the other three cases of neuron number. For water production rate, 8 hidden neurons were observed to be optimal compared to other cases. Oil and water production rate for a peak NPV value of 3 billion US dollars was recorded to be 2000m 3 /day and 4500m 3 /day respectively. The response was optimal for all cases except for the net present value, which requires a more substantial amount of data for the neural network model.

Prediction of Rate of Penetration for wells at Nam Con Son basin using Artificial Neural Networks models

2023

The rate of penetration (ROP) is an important parameter that affects the success of a drilling operation. In this paper, the research approach is based on different artificial neural network (ANN) models to predict ROP for oil and gas wells in Nam Con Son basin. The first is the process of collecting and evaluating drilling parameters as input data of the model. Next is to find the network model capable of predicting ROP most accurately. After that, the study will evaluate the number of input parameters of the network model. The ROP prediction results obtained from different ANN models are also compared with traditional models such as the Bingham model, Bourgoyne & Young model. These results have shown the competitiveness of the ANN model and its high applicability to actual drilling operations.

Applicability of an Artificial Neural Network for Predicting Water-Alternating-CO2 Performance

Energies, 2017

The injection of CO2 as part of the water-alternating-gas (WAG) process has been widely employed in many mature oil fields for effectively enhancing oil production and sequestrating carbon permanently inside the reservoirs. In addition to simulations, the use of intelligent tools is of particular interest for evaluating the uncertainties in the WAG process and predicting technical or economic performance. This study proposed the comprehensive evaluations of a water-alternating-CO2 process utilizing the artificial neural network (ANN) models that were initially generated from a qualified numerical data set. Totally two uncertain reservoir parameters and three installed surface operating factors were designed as input variables in each of the three-layer ANN models to predicting a series of WAG production performances after 5, 15, 25, and 35 injection cycles. In terms of the technical view point, the relationships among parameters and important outputs, including oil recovery, CO2 pro...

Studying the Capabilities of Two Neural Network Models in Optimization of Hydrocarbon Reservoir Management

2015

Porosity is one of the most significant parameters of hydrocarbon reservoirs describing the quality of reservoirs rocks. It is one of the most crucial characteristics that need to be predicted for evaluation of reservoirs. The conventional methods for porosity determination are core analysis and well test technique. These methods are however very expensive and time-consuming tasks. One of the comparatively inexpensive and readily available sources of inferring porosity is nuclear magnetic resonance (NMR) log. The aim of this paper is to present an application of two machine learning methodologies, which are christened general regression neural network (GRNN) and back-propagation neural network (BPNN), for prediction of NMR porosity using well log data and intelligent models. Available data of three was considered for training and testing the networks. Verification process was also performed by one remaining well. Obtained results have shown that the overall correlation coefficients ...

Research Article Oil Well Characterization and Artificial Gas Lift Optimization Using Neural Networks Combined with Genetic Algorithm

This paper examines the characterization of six oil wells and the allocation of gas considering limited and unlimited case scenario. Artificial gas lift involves injecting high-pressured gas from the surface into the producing fluid column through one or more subsurface valves set at predetermined depths. This improves recovery by reducing the bottom-hole pressure at which wells become uneconomical and are thus abandoned. This paper presents a successive application of modified artificial neural network (MANN) combined with a mild intrusive genetic algorithm (MIGA) to the oil well characteristics with promising results. This method helps to prevent the overallocation of gas to wells for recovery purposes while also maximizing oil production by ensuring that computed allocation configuration ensures maximum economic accrual. Results obtained show marked improvements in the allocation especially in terms of economic returns.

Prediction of the Rate of Penetration while Drilling Horizontal Carbonate Reservoirs Using the Self-Adaptive Artificial Neural Networks Technique

Sustainability

Rate of penetration (ROP) is one of the most important drilling parameters for optimizing the cost of drilling hydrocarbon wells. In this study, a new empirical correlation based on an optimized artificial neural network (ANN) model was developed to predict ROP alongside horizontal drilling of carbonate reservoirs as a function of drilling parameters, such as rotation speed, torque, and weight-on-bit, combined with conventional well logs, including gamma-ray, deep resistivity, and formation bulk density. The ANN model was trained using 3000 data points collected from Well-A and optimized using the self-adaptive differential evolution (SaDE) algorithm. The optimized ANN model predicted ROP for the training dataset with an average absolute percentage error (AAPE) of 5.12% and a correlation coefficient (R) of 0.960. A new empirical correlation for ROP was developed based on the weights and biases of the optimized ANN model. The developed correlation was tested on another dataset collec...

Optimizing the location of the gas injection well during gas assisted gravity drainage in a fractured carbonate reservoir using artificial intelligence

Theoretical Foundations of Chemical Engineering, 2017

⎯Gas assisted gravity drainage (GAGD) is a novel subdivision of gas injection method. In this method the injection wells are located in the upper bed of the oil zone, and the production wells are drilled at the bottom bed of the oil zone. Reservoir simulation is among the decision tools for investigating production rate and selecting the best scenarios for developing the oil and gas fields. Selecting the location of the injection wells for reaching the optimized pressure and production rate is one of the most significant challenges during the injection process. Recent experiences have shown that artificial intelligence (AI) is a reliable solution for taking the mentioned decision appropriately and in a least possible time. This study is attributed to the investigation of applying the artificial neural network (ANN) as an artificial intelligence method and a potent predictor for choosing the most proper location for injection in a GAGD process in a fractured carbonate reservoir. The results of this investigation clearly show the efficiency of the ANN as a powerful tool for optimizing the location of the injection wells in a GAGD process. The comparison between the results of ANN and black oil simulator indicated that the predictions obtained from the ANN is highly reliable. In fact the production flow rate and pressure can be obtained in every possible location of the injection well.

Metaheuristic algorithm integrated neural networks for well-test analyses of petroleum reservoirs

Scientific Reports

In recent years, well-test research has witnessed several works to automate reservoir model identification and characterization using computer-assisted models. Since the reservoir model identification is a classification problem, while its characterization is a regression-based task, their simultaneous accomplishment is always challenging. This work combines genetic algorithm optimization and artificial neural networks to identify and characterize homogeneous reservoir systems from well-testing data automatically. A total of eight prediction models, including two classifiers and six regressors, have been trained. The simulated well-test pressure derivatives with varying noise percentages comprise the training samples. The feature selection and hyperparameter tuning have been performed carefully using the genetic algorithm to enhance the prediction accuracy. The models were validated using nine simulated and one real-field test case. The optimized classifier identifies all the reserv...

A Workflow Incorporating an Artificial Neural Network to Predict Subsurface Porosity for CO2 Storage Geological Site Characterization

Processes

The large scale and complexity of Carbon, Capture, Storage (CCS) projects necessitates time and cost saving strategies to strengthen investment and widespread deployment of this technology. Here, we successfully demonstrate a novel geologic site characterization workflow using an Artificial Neural Network (ANN) at the Southeast Regional Carbon Anthropogenic Test in Citronelle, Alabama. The Anthropogenic Test Site occurs within the Citronelle oilfield which contains hundreds of wells with electrical logs that lack critical porosity measurements. Three new test wells were drilled at the injection site and each well was paired with a nearby legacy well containing vintage electrical logs. The test wells were logged for measurements of density porosity and cored over the storage reservoir. An Artificial Neural Network was developed, trained, and validated using patterns recognized between the between vintage electrical logs and modern density porosity measurements at each well pair. The ...

Oil Well Characterization and Artificial Gas Lift Optimization Using Neural Networks Combined with Genetic Algorithm

This paper examines the characterization of six oil wells and the allocation of gas considering limited and unlimited case scenario. Artificial gas lift involves injecting high-pressured gas from the surface into the producing fluid column through one or more subsurface valves set at predetermined depths. This improves recovery by reducing the bottom-hole pressure at which wells become uneconomical and are thus abandoned. This paper presents a successive application of modified artificial neural network (MANN) combined with a mild intrusive genetic algorithm (MIGA) to the oil well characteristics with promising results. This method helps to prevent the overallocation of gas to wells for recovery purposes while also maximizing oil production by ensuring that computed allocation configuration ensures maximum economic accrual. Results obtained show marked improvements in the allocation especially in terms of economic returns.