Can’t fit a model to the experimental semivariogram

I’m trying to solve a problem related to geology using kriging.js. As you know, creating a prediction surface with kriging involve variogram (experimental semivariogram and its model) to estimate the spatial autocorrelation of the data. However, the selected model fails to accurately fit the experimental semivariogram (sometimes not displayed).
Here is my code to plot the variogram models :

 // Variogram models
    sphericalModel(h, nugget, range, sill) {
        if (h > range) return nugget + sill;
        return nugget + sill * (1.5 * (h / range) - 0.5 * (h / range) ** 3);
    }

    exponentialModel(h, nugget, range, sill) {
        return nugget + sill * (1 - Math.exp(-h / range));
    }

    gaussianModel(h, nugget, range, sill) {
        return nugget + sill * (1 - Math.exp(-((h ** 2) / (range ** 2))));
    }

    fitVariogramModel(model = 'spherical') {
        const { distances, semivariances } = this.gamma;
        const nugget = 0;
        const sill = Math.max(...semivariances);
        const range = Math.max(...distances);

        const modelFunction = model === 'spherical' ? this.sphericalModel :
                              model === 'exponential' ? this.exponentialModel :
                              this.gaussianModel;

        const errorFunction = (params) => {
            const [nugget, range, sill] = params;
            return distances.reduce((sum, h, i) => {
                return sum + (semivariances[i] - modelFunction(h, nugget, range, sill)) ** 2;
            }, 0);
        };
        this.model = { modelFunction, params: [nugget, range, sill] };
    }

Full code : https://jsfiddle.net/qLdayvsr/8/

Expected results :